# Install the required libraries for the project
!pip install openai
!pip install pandas
!pip install numpy
!pip install matplotlib
!pip install seaborn
!pip install scikit-learn
!pip install nltk
!pip install wordcloud
!pip install textblob
!pip install transformers
!pip install torch
!pip install warnings

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ERROR: Could not find a version that satisfies the requirement warnings (from versions: none)
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# Import the required libraries for the project
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from nltk.corpus import stopwords
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score, confusion_matrix
import matplotlib.pyplot as plt
from wordcloud import WordCloud
import warnings
warnings.filterwarnings('ignore')

DATA_PATH = "/content/Dataset - Real-Time Retail Feedback Intelligence.csv"
review = pd.read_csv(DATA_PATH, sep=None, engine='python', on_bad_lines='warn')
review.head()

print('\nThis is the descripton\n', review.describe(), '\n')
print('\nThis is the info\n', review.info(), '\n')
print('\nThis is the null values\n', review.isnull().sum(), '\n')

This is the descripton
          Unnamed: 0   Clothing.ID           Age        Rating  \
count  23486.000000  23486.000000  23486.000000  23486.000000   
mean   11743.500000    918.118709     43.198544      4.196032   
std     6779.968547    203.298980     12.279544      1.110031   
min        1.000000      0.000000     18.000000      1.000000   
25%     5872.250000    861.000000     34.000000      4.000000   
50%    11743.500000    936.000000     41.000000      5.000000   
75%    17614.750000   1078.000000     52.000000      5.000000   
max    23486.000000   1205.000000     99.000000      5.000000   

       Recommended.IND  Positive.Feedback.Count  
count     23486.000000             23486.000000  
mean          0.822362                 2.535936  
std           0.382216                 5.702202  
min           0.000000                 0.000000  
25%           1.000000                 0.000000  
50%           1.000000                 1.000000  
75%           1.000000                 3.000000  
max           1.000000               122.000000   

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 23486 entries, 0 to 23485
Data columns (total 11 columns):
 #   Column                   Non-Null Count  Dtype 
---  ------                   --------------  ----- 
 0   Unnamed: 0               23486 non-null  int64 
 1   Clothing.ID              23486 non-null  int64 
 2   Age                      23486 non-null  int64 
 3   Title                    19676 non-null  object
 4   Review.Text              22641 non-null  object
 5   Rating                   23486 non-null  int64 
 6   Recommended.IND          23486 non-null  int64 
 7   Positive.Feedback.Count  23486 non-null  int64 
 8   Division.Name            23472 non-null  object
 9   Department.Name          23472 non-null  object
 10  Class.Name               23472 non-null  object
dtypes: int64(6), object(5)
memory usage: 2.0+ MB

This is the info
 None 


This is the null values
 Unnamed: 0                    0
Clothing.ID                   0
Age                           0
Title                      3810
Review.Text                 845
Rating                        0
Recommended.IND               0
Positive.Feedback.Count       0
Division.Name                14
Department.Name              14
Class.Name                   14
dtype: int64

review1 = review.copy()
review1['Title'].fillna('My Review', inplace=True)
review1.head()
#review1['Review Text'].fillna('No Review', inplace=True

review1.isnull().sum()

no_review1 = review1[review1['Review.Text'].isnull()]
print(no_review1.shape)
no_review1.head()

(845, 11)

review1.dropna(inplace=True)
review1.isnull().sum()

review2 = review1.copy()
# Drop the 'Unnamed: 0' column directly from the DataFrame
review2 = review2.drop('Unnamed: 0', axis=1)
review2.info()

<class 'pandas.core.frame.DataFrame'>
Index: 22628 entries, 0 to 23485
Data columns (total 10 columns):
 #   Column                   Non-Null Count  Dtype 
---  ------                   --------------  ----- 
 0   Clothing.ID              22628 non-null  int64 
 1   Age                      22628 non-null  int64 
 2   Title                    22628 non-null  object
 3   Review.Text              22628 non-null  object
 4   Rating                   22628 non-null  int64 
 5   Recommended.IND          22628 non-null  int64 
 6   Positive.Feedback.Count  22628 non-null  int64 
 7   Division.Name            22628 non-null  object
 8   Department.Name          22628 non-null  object
 9   Class.Name               22628 non-null  object
dtypes: int64(5), object(5)
memory usage: 1.9+ MB

print(type(review2['Title'][100]))
print(review2['Review.Text'][100])

<class 'str'>
At first i wasn't sure about it. the neckline is much lower and wavy than i thought. but after wearing it, it really is comfortable. it stretches a lot, so i wear a cami underneath so when i lean forward i'm not showing the world my torso.

review2['Review.Text'] = review2['Review.Text'].astype(str)
review2['Title'] = review2['Title'].astype(str)
review2['Division.Name'] = review2['Division.Name'].astype(str)
review2['Department.Name'] = review2['Department.Name'].astype(str)
review2['Class.Name'] = review2['Class.Name'].astype(str)
review2 = review2.reset_index()
review2.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 22628 entries, 0 to 22627
Data columns (total 11 columns):
 #   Column                   Non-Null Count  Dtype 
---  ------                   --------------  ----- 
 0   index                    22628 non-null  int64 
 1   Clothing.ID              22628 non-null  int64 
 2   Age                      22628 non-null  int64 
 3   Title                    22628 non-null  object
 4   Review.Text              22628 non-null  object
 5   Rating                   22628 non-null  int64 
 6   Recommended.IND          22628 non-null  int64 
 7   Positive.Feedback.Count  22628 non-null  int64 
 8   Division.Name            22628 non-null  object
 9   Department.Name          22628 non-null  object
 10  Class.Name               22628 non-null  object
dtypes: int64(6), object(5)
memory usage: 1.9+ MB

def histogram_boxplot(data, feature, figsize=(12,7), kde=True, bins=None):
    figure, (ax_box, ax_hist) = plt.subplots(2, sharex=False, figsize=figsize)
    #figure.set_facecolor('black')

    #plt.title(feature, color='white')

    sns.boxplot(data=data, x=feature, ax=ax_box, showmeans=True, color='violet')
    if bins == None:
        sns.set_style('dark')
        sns.histplot(data=data, x=feature, kde=kde, ax=ax_hist)
    else:
        sns.set_style('dark')
        sns.histplot(data=data, x=feature, kde=kde, ax=ax_hist, bins=bins)

    ax_hist.axvline(data[feature].mean(), color='green', linestyle='--')
    ax_hist.axvline(data[feature].median(), color='black', linestyle='-')

    """
    plt.title(feature, color='white')
    plt.xticks(rotation=0, color='white')
    plt.yticks(color='white')
    plt.xlabel(feature, color='white')
    plt.ylabel('count', color='white')
    """

    plt.show()

histogram_boxplot(review2, 'Rating', figsize=(12,7), kde=True, bins=None)

histogram_boxplot(review2, 'Age', figsize=(12,7), kde=True, bins=None)

def labeled_barplot(data, feature, perc=False, n=None):
    total = len(data[feature])
    count = data[feature].nunique()
    #
    if n is None:
        plt.figure(figsize=(count + 1, 5), facecolor='black')
        plt.title(f'{feature}', color='white')
        plt.xticks(rotation=30, fontsize=15, color='white')
        plt.yticks(fontsize=15, color='white')
        plt.xlabel(feature, color='white')
        plt.ylabel('count', color='white')
    else:
        plt.figure(figsize=(n + 1, 5), facecolor='black')
        plt.title(f'{feature}', color='white')
        plt.xticks(rotation=30, fontsize=15, color='white')
        plt.yticks(fontsize=15, color='white')
        plt.xlabel(feature, color='white')
        plt.ylabel('count', color='white')

    sns.set_style('dark')
    ax = sns.countplot(
        data=data,
        x=feature,
        palette='Paired',
        order=data[feature].value_counts().index[:n].sort_values(),
    )

    ax.xaxis.set_ticklabels(ax.get_xticklabels(), rotation=30, color='white')
    ax.yaxis.set_ticklabels(ax.get_yticklabels(), rotation=0, color='white')

    for p in ax.patches:
        if perc == True:
            label = '{:.1f}%'.format(
                100*p.get_height()/total
            )
        else:
            label = p.get_height()

        x = p.get_x() + p.get_width()/2
        y = p.get_height()

        ax.annotate(
            label,
            (x, y),
            ha='center',
            va='center',
            #color='white',
            size=12,
            xytext=(0,5),
            textcoords='offset points',
        )
    plt.show()

labeled_barplot(review2, 'Rating')

labeled_barplot(review2, 'Rating', perc=True)

labeled_barplot(review2, 'Division.Name')

labeled_barplot(review2, 'Department.Name')

labeled_barplot(review2, 'Class.Name')

labeled_barplot(review2, 'Recommended.IND')

def top_ten_type_graph(data, feature):
    top_ten = data.nlargest(10, feature)

    fig, ax = plt.subplots(facecolor='black')
    plt.title('highest ' + f'{feature}', color = 'white')
    sns.barplot(data=top_ten, x='Class.Name', y=top_ten[feature])
    plt.yticks(fontsize=8, color='white')
    plt.xticks(fontsize=8, color='white')
    plt.xlabel(feature, color='white')
    plt.ylabel(feature, color='white')

    ax.bar_label(container=ax.containers[0], label_type='edge')

    plt.show()

top_ten_type_graph(review2, 'Positive.Feedback.Count')

top_ten_type_graph(review2, 'Age')

review2.head()

def labeled_barplot1(data, feature, target, perc=False, n=None):
    total = len(data[feature])
    count = data[feature].nunique()
    if n is None:
        plt.figure(figsize=(count + 1, 5), facecolor='black')
    else:
        plt.figure(figsize=(n + 1, 5), facecolor='black')

    plt.title(f'{target}' + ' vs ' + f'{feature}', color='white')
    plt.xticks(rotation=30, fontsize=15, color='white')
    plt.yticks(fontsize=15, color='white')
    plt.xlabel(feature, color='white')
    plt.ylabel(target, color='white')
    sns.set_style('dark')
    ax = sns.barplot(
        data=data,
        x=feature,
        y=target,
        palette='Paired',
        order=data[feature].value_counts().index[:n].sort_values(),
        #edgecolor='black',
    )

    for p in ax.patches:
        if perc == True:
            label = '{:.1f}%'.format(
                100*p.get_height()/total
            )
        else:
            label = '{:.3f}'.format(p.get_height())

        x = p.get_x() + p.get_width()/2
        y = p.get_height()

        ax.annotate(
            label,
            (x, y),
            #ha='center',
            va='center',
            size=12,
            xytext=(0,5),
            textcoords='offset points',
        )
    plt.show()

labeled_barplot1(review2, 'Division.Name', 'Positive.Feedback.Count')

labeled_barplot1(review2, 'Department.Name', 'Recommended.IND')

from textblob import TextBlob
blob1 = TextBlob(review2['Review.Text'][0])
print(blob1.sentiment)

Sentiment(polarity=0.6333333333333333, subjectivity=0.9333333333333332)

from wordcloud import WordCloud, STOPWORDS
stopwords = set(STOPWORDS)
import matplotlib.pyplot as plt

for i in range(0, 10):
  text = review2['Review.Text'][i]
  wordcloud = WordCloud(
      stopwords=stopwords,
      background_color='white',
      max_words=1000,
      collocations=False,
      width=800, # Increased width for better rendering
      height=400 # Added height for a more balanced canvas
   ).generate(text)
  blob = TextBlob(text)
  print('\n\n\nThis is the sentiment and the opinion for review with index', i, '\n', blob.sentiment)
  plt.figure(figsize=(10,10))
  plt.imshow(wordcloud, interpolation='bilinear')
  plt.axis('off')
  plt.show()



This is the sentiment and the opinion for review with index 0 
 Sentiment(polarity=0.6333333333333333, subjectivity=0.9333333333333332)



This is the sentiment and the opinion for review with index 1 
 Sentiment(polarity=0.33958333333333335, subjectivity=0.7250000000000001)



This is the sentiment and the opinion for review with index 2 
 Sentiment(polarity=0.07367494824016563, subjectivity=0.35629399585921323)



This is the sentiment and the opinion for review with index 3 
 Sentiment(polarity=0.5499999999999999, subjectivity=0.625)



This is the sentiment and the opinion for review with index 4 
 Sentiment(polarity=0.512890625, subjectivity=0.56875)



This is the sentiment and the opinion for review with index 5 
 Sentiment(polarity=0.17874999999999996, subjectivity=0.533125)



This is the sentiment and the opinion for review with index 6 
 Sentiment(polarity=0.13375, subjectivity=0.6077777777777778)



This is the sentiment and the opinion for review with index 7 
 Sentiment(polarity=0.17163461538461539, subjectivity=0.49615384615384617)



This is the sentiment and the opinion for review with index 8 
 Sentiment(polarity=0.0025000000000000022, subjectivity=0.38)



This is the sentiment and the opinion for review with index 9 
 Sentiment(polarity=0.20419973544973546, subjectivity=0.46725681725681717)

chunk_size = 2261
chunks = []

for i in range(0, len(review2), chunk_size):
    chunk = review2.iloc[i:i+chunk_size]
    start = i
    stop = i + chunk_size - 1
    chunk['chunk_start'] = start
    chunk['chunk_stop'] = stop
    chunks.append(chunk)

for i, chunk in enumerate(chunks):
  text = review2['Review.Text'][i]
  wordcloud = WordCloud(
      stopwords=stopwords,
      background_color='white',
      max_words=1000,
      collocations=False,
      width=800, # Increased width for better rendering
      height=400 # Added height for a more balanced canvas
   ).generate(text)
  blob = TextBlob(text)
  print('\n\n\nThis is the sentiment and the opinion for reviews from index ', i + chunk_size - 2261, ' to index ', i + chunk_size + 1, '\n', blob.sentiment)
  plt.figure(figsize=(10,10))
  plt.imshow(wordcloud, interpolation='bilinear')
  plt.axis('off')
  plt.show()



This is the sentiment and the opinion for reviews from index  0  to index  2262 
 Sentiment(polarity=0.6333333333333333, subjectivity=0.9333333333333332)



This is the sentiment and the opinion for reviews from index  1  to index  2263 
 Sentiment(polarity=0.33958333333333335, subjectivity=0.7250000000000001)



This is the sentiment and the opinion for reviews from index  2  to index  2264 
 Sentiment(polarity=0.07367494824016563, subjectivity=0.35629399585921323)



This is the sentiment and the opinion for reviews from index  3  to index  2265 
 Sentiment(polarity=0.5499999999999999, subjectivity=0.625)



This is the sentiment and the opinion for reviews from index  4  to index  2266 
 Sentiment(polarity=0.512890625, subjectivity=0.56875)



This is the sentiment and the opinion for reviews from index  5  to index  2267 
 Sentiment(polarity=0.17874999999999996, subjectivity=0.533125)



This is the sentiment and the opinion for reviews from index  6  to index  2268 
 Sentiment(polarity=0.13375, subjectivity=0.6077777777777778)



This is the sentiment and the opinion for reviews from index  7  to index  2269 
 Sentiment(polarity=0.17163461538461539, subjectivity=0.49615384615384617)



This is the sentiment and the opinion for reviews from index  8  to index  2270 
 Sentiment(polarity=0.0025000000000000022, subjectivity=0.38)



This is the sentiment and the opinion for reviews from index  9  to index  2271 
 Sentiment(polarity=0.20419973544973546, subjectivity=0.46725681725681717)

!pip install openai
!pip install tiktoken
!pip install pypdf
!pip install random

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Collecting pypdf
  Downloading pypdf-6.6.0-py3-none-any.whl.metadata (7.1 kB)
Downloading pypdf-6.6.0-py3-none-any.whl (328 kB)
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Installing collected packages: pypdf
Successfully installed pypdf-6.6.0
ERROR: Could not find a version that satisfies the requirement random (from versions: none)
ERROR: No matching distribution found for random

# Importing the necessary libraries
# Mandatory: Run this AFTER restarting the runtime following the installation step.

import os                   # File paths, environment variables
import json                 # JSON handling
import time                 # Timing utilities
from datetime import datetime

import numpy as np          # Numerical operations (used in RAG similarity search)
import pandas as pd         # DataFrame loading and manipulation
from tqdm import tqdm       # Progress bars for iteration and apply()

# OpenAI-compatible client (used to call Gemini models via Generative Language API)
from openai import OpenAI

# Tokenizer utilities (for token-aware chunking)
import tiktoken

# PDF parsing (extract text from HR policy PDFs)
import pypdf


import random

print("All libraries imported successfully.")

All libraries imported successfully.

from openai import OpenAI
import os
from google.colab import userdata
OpenAI_API_KEY =userdata.get('my_key')
OPENAI_CUSTOM_BASE_URL=userdata.get('my_url')

client = OpenAI(base_url=OPENAI_CUSTOM_BASE_URL, api_key=OpenAI_API_KEY)

emb_resp = client.embeddings.create(
  #model='text-embedding-ada-002',
  model='text-embedding-3-small',
  input='Your text string goes here',
)

#this code will be kept commented out to not use up tokens
"""
emb_vector = emb_resp.data[0].embedding
print('This is the embedding vector:\n', emb_vector, '\n\n')
print("\nEmbedding generated. Vector length:\n", len(emb_vector), "\n")

"""

'\nemb_vector = emb_resp.data[0].embedding\nprint(\'This is the embedding vector:\n\', emb_vector, \'\n\n\')\nprint("\nEmbedding generated. Vector length:\n", len(emb_vector), "\n")\n\n'

import tiktoken

def num_tokens_from_string(string: str, encoding_name: str) -> int:
    """Returns the number of tokens in a text string."""
    encoding = tiktoken.get_encoding(encoding_name)
    num_tokens = len(encoding.encode(string))
    return num_tokens

# 'cl100k_base' is a common encoding for newer GPT models like gpt-4o-mini
num_tokens_from_string("Your text string goes here", "o200k_base")

5

print("Chat completion test (gpt-4o-mini)...")
chat_resp = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Explain to me how AI works in one short sentence."}
    ],
)

# Print the assistant reply text

#assistant_text = chat_resp.choices[0].message.content
#print("Assistant reply:\n", assistant_text, "\n")

Chat completion test (gpt-4o-mini)...

# Installing the necessary libraries with specified versions
!pip install -q openai==1.66.3 \
                tqdm==4.67.0 \
                tiktoken==0.9.0 \
                pypdf==5.4.0 \
                langchain==0.3.20 \
                langchain-community==0.3.19 \
                langchain-chroma==0.2.2 \
                langchain-openai==0.3.9 \
                chromadb==0.6.3 \
                langchain-text-splitters==0.0.1

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ERROR: Cannot install langchain-text-splitters==0.0.1 and langchain==0.3.20 because these package versions have conflicting dependencies.
ERROR: ResolutionImpossible: for help visit https://pip.pypa.io/en/latest/topics/dependency-resolution/#dealing-with-dependency-conflicts

DATA_PATH = "/content/Dataset - Real-Time Retail Feedback Intelligence.csv"
reviews3 = pd.read_csv(DATA_PATH, sep=None, engine='python', on_bad_lines='warn')
reviews3.head()

reviews3['Title'].fillna('My Review', inplace=True)
reviews3.head()
#review1['Review Text'].fillna('No Review', inplace=True

/tmp/ipython-input-2004461624.py:1: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.

For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.


  reviews3['Title'].fillna('My Review', inplace=True)

reviews3.isnull().sum()

no_reviews3 = reviews3[reviews3['Review.Text'].isnull()]
print(no_reviews3.shape)
no_reviews3.head()

(845, 11)

reviews3.dropna(inplace=True)
reviews3.isnull().sum()

reviews4 = reviews3.copy()
# Drop the 'Unnamed: 0' column directly from the DataFrame
reviews4 = reviews4.drop('Unnamed: 0', axis=1)
reviews4 = reviews4.reset_index(drop=True)
reviews4.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 22628 entries, 0 to 22627
Data columns (total 10 columns):
 #   Column                   Non-Null Count  Dtype 
---  ------                   --------------  ----- 
 0   Clothing.ID              22628 non-null  int64 
 1   Age                      22628 non-null  int64 
 2   Title                    22628 non-null  object
 3   Review.Text              22628 non-null  object
 4   Rating                   22628 non-null  int64 
 5   Recommended.IND          22628 non-null  int64 
 6   Positive.Feedback.Count  22628 non-null  int64 
 7   Division.Name            22628 non-null  object
 8   Department.Name          22628 non-null  object
 9   Class.Name               22628 non-null  object
dtypes: int64(5), object(5)
memory usage: 1.7+ MB

print(type(reviews4['Title'][100]))
print(reviews4['Review.Text'][100])

<class 'str'>
The fabric felt cheap and i didn't find it to be a flattering top. for reference i am wearing a medium in the photos and my measurements are 38-30-40.

reviews4['Review.Text'] = reviews4['Review.Text'].astype(str)
reviews4['Title'] = reviews4['Title'].astype(str)
reviews4['Division.Name'] = reviews4['Division.Name'].astype(str)
reviews4['Department.Name'] = reviews4['Department.Name'].astype(str)
reviews4['Class.Name'] = reviews4['Class.Name'].astype(str)
reviews4 = reviews4.reset_index()
reviews4.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 22628 entries, 0 to 22627
Data columns (total 11 columns):
 #   Column                   Non-Null Count  Dtype 
---  ------                   --------------  ----- 
 0   index                    22628 non-null  int64 
 1   Clothing.ID              22628 non-null  int64 
 2   Age                      22628 non-null  int64 
 3   Title                    22628 non-null  object
 4   Review.Text              22628 non-null  object
 5   Rating                   22628 non-null  int64 
 6   Recommended.IND          22628 non-null  int64 
 7   Positive.Feedback.Count  22628 non-null  int64 
 8   Division.Name            22628 non-null  object
 9   Department.Name          22628 non-null  object
 10  Class.Name               22628 non-null  object
dtypes: int64(6), object(5)
memory usage: 1.9+ MB

five_samples = reviews4.sample(5)
five_samples

div_name = reviews4['Division.Name'][0]
print(div_name)

dept_name = reviews4['Department.Name'][0]
print(dept_name)

class_name = reviews4['Class.Name'][0]
print(class_name)

review_text = reviews4['Review.Text'][0]
print(review_text)

Initmates
Intimate
Intimates
Absolutely wonderful - silky and sexy and comfortable

five_samples = reviews4.sample(5)
five_samples['Department.Name']

def review_prompt_five():
    """
    Category, Sentiment, Summary, Personalized Message, and Retail Insight

    """
    for i in range(5):
      five_samples = reviews4.sample()
      review_text = five_samples['Review.Text']
      div_name = five_samples['Division.Name']
      dept_name = five_samples['Department.Name']
      class_name = five_samples['Class.Name']
      print(f'\nThse are the parts\n Division: {div_name} \n Department: {dept_name} \n Class: {class_name}\n')
      #print('\nThis is the department\n', dept_name, '\n')
      #print('\nThis is the class\n', class_name, '\n')

    #return review_text, div_name, dept_name, class_name

review_prompt_five()

Thse are the parts
 Division: 11918    General
Name: Division.Name, dtype: object 
 Department: 11918    Tops
Name: Department.Name, dtype: object 
 Class: 11918    Knits
Name: Class.Name, dtype: object


Thse are the parts
 Division: 15339    General Petite
Name: Division.Name, dtype: object 
 Department: 15339    Tops
Name: Department.Name, dtype: object 
 Class: 15339    Fine gauge
Name: Class.Name, dtype: object


Thse are the parts
 Division: 19874    General
Name: Division.Name, dtype: object 
 Department: 19874    Jackets
Name: Department.Name, dtype: object 
 Class: 19874    Outerwear
Name: Class.Name, dtype: object


Thse are the parts
 Division: 5674    General Petite
Name: Division.Name, dtype: object 
 Department: 5674    Tops
Name: Department.Name, dtype: object 
 Class: 5674    Blouses
Name: Class.Name, dtype: object


Thse are the parts
 Division: 20396    General
Name: Division.Name, dtype: object 
 Department: 20396    Bottoms
Name: Department.Name, dtype: object 
 Class: 20396    Pants
Name: Class.Name, dtype: object

print(review_prompt_five())

Thse are the parts
 Division: 4911    General
Name: Division.Name, dtype: object 
 Department: 4911    Tops
Name: Department.Name, dtype: object 
 Class: 4911    Knits
Name: Class.Name, dtype: object


Thse are the parts
 Division: 21995    General
Name: Division.Name, dtype: object 
 Department: 21995    Tops
Name: Department.Name, dtype: object 
 Class: 21995    Sweaters
Name: Class.Name, dtype: object


Thse are the parts
 Division: 7279    General
Name: Division.Name, dtype: object 
 Department: 7279    Tops
Name: Department.Name, dtype: object 
 Class: 7279    Knits
Name: Class.Name, dtype: object


Thse are the parts
 Division: 14294    General
Name: Division.Name, dtype: object 
 Department: 14294    Tops
Name: Department.Name, dtype: object 
 Class: 14294    Knits
Name: Class.Name, dtype: object


Thse are the parts
 Division: 1174    General Petite
Name: Division.Name, dtype: object 
 Department: 1174    Tops
Name: Department.Name, dtype: object 
 Class: 1174    Blouses
Name: Class.Name, dtype: object

None

SYSTEM_MESSAGE_CS0 = """
You are a strict, expert retail product review analyst. Prepare a short report
with the following five elements, outputting the response in JSON format:
  1. The categories: this should be an object with four entries: 'division', 'department', 'class' and 'Mt Category'.
  2. The sentiment: this should be a string indicating 'positive', 'neutral', or 'negative'. If more than one sentiment applies, prioritize the dominant one.
  3. The summary: this is a short summary of the review.
  4. The personalized_message: a personalized message composed in response to the review.
  5. The retail_insight: any insights that can improve customer satisfaction.
"""

BASE_MESSAGES_CS0 = [
    {"role": "system", "content": SYSTEM_MESSAGE_CS0},
]

#input for llm-as-a-judge
def review_prompt_five3():
    """
    Samples five reviews and returns them as a list of dictionaries,
    each representing a single review.
    """
    five_samples_df = reviews4.sample(n=50) # Get five random rows as a DataFrame
    list_of_review_dicts = []
    for index, row in five_samples_df.iterrows():
        list_of_review_dicts.append({
            "div_name": row['Division.Name'],
            "dept_name": row['Department.Name'],
            "class_name": row['Class.Name'],
            "review_text": row['Review.Text'],
            "review_index": row['index']
        })
    return list_of_review_dicts

from nltk.corpus.reader import norm
def zero_shot_prompt(model_name: str = "gpt-4o-mini", review_data: dict = None) -> tuple:
    """
    Generates a Zero-Shot prompt response based on the provided review data.
    Returns the raw LLM output, the original user content string, and the review index.
    """
    if review_data is None:
        review_data = review_prompt_one2()

    user_content = (
        f"Division: {review_data['div_name']}\n"
        f"Department: {review_data['dept_name']}\n"
        f"Class: {review_data['class_name']}\n"
        f"Review: {review_data['review_text']}"
        f"Index: {review_data['review_index']}"
    )

    instructions = BASE_MESSAGES_CS0 + [{
        "role": "user",
        "content": user_content
    }]

    response = client.chat.completions.create(
        model=model_name,
        messages=instructions,
        temperature=0.0,
        max_tokens=300
    )

    raw_info = response.choices[0].message.content.strip()
    #print("This is the actual review:\n", review_data['review_text'], '\n\n')

    return {'raw_info':raw_info}, {'user_content': user_content}, review_data['review_index'] # Return raw_info dict, user_content string, and review_index

# Call review_prompt_five3() to get a list of five review samples
list_of_review_samples = review_prompt_five3()

# Process each review sample with zero_shot_prompt and collect outputs
llm_outputs_parsed_list = [] # Changed name to be more descriptive
for sample_data in list_of_review_samples:
    raw_info_dict, user_content_dict, review_idx = zero_shot_prompt(review_data=sample_data)
    llm_outputs_parsed_list.append({
        "raw_llm_output": raw_info_dict['raw_info'],
        "original_user_content": user_content_dict['user_content'],
        "review_index": review_idx
    })

# Create a DataFrame from the parsed outputs
zero_shot_results_df = pd.DataFrame(llm_outputs_parsed_list)
zero_shot_results_df.head()

This is the actual review:
 It takes perfection for me to pay this much for a pair of pants, and these delivered! i bought these pants love-at-first-sight in the carbon color. i have not washed them yet, so i can't comment on durability, but they fit like a dream. i carry extra weight in my thighs and rear, and these pants are very flattering and slimming, and don't pull and crease in the crotch area like so many other pants do on my body. my usual size fit perfectly, and the length is great on my 5'5" frame. i can almost 


This is the actual review:
 I bought this in late august and have been wearing it ever since. i love that you can wear it either on or off-the-shoulder. the second option adds sexiness even though the top is flowy, comfortable, and easy to wear. on the shoulder i've even worn it to work on casual friday. the print is versatile, and i like that the fabric has a little heft while still being soft and drapey. i definitely recommend, especially since i notice it is now on sale!! it is generously sized, so i'd recommend erring 


This is the actual review:
 I wish we had cooler weather in california so i could wear this more often. i bought this sweater/coat on sale plus an extra 20% off so i got a great deal on it. i love this sweater! it looks matronly in the picture but it looks very chic and sophisticated in person. the sleeves hit above the wrist as shown on the model. it does have pockets. it's the right weight for chilly weather. 


This is the actual review:
 I actually love these jeans. i feel they run true to size. i actually love the button detailing at the bottom. makes them unique and different from the other jeans in my closet. i can wear them dressed up with heels or wear with sandals. 


This is the actual review:
 I am a fan of these designers and their gorgeous fabrics - most of which are heavy brocades. this is so pretty and fun. the print is playful, and colors are gorgeous, and the fit is floaty but not too full or too tight. i love the bit of beading, and that i can dress it up or down.

i am petite and not usually a bigger print or long dress fan, but i know that i will wear this one for years. 

i did get a 00 petite as i know that only this small size fits me at retailer - 0's swallow me. this i 


This is the actual review:
 I wear a typical 25 in coh jeans and i ordered these in a 25. they fit but they feel more like jeggings than skinny jeans. the wash isn't as faded out as in the picture. and at 5'2" the rip is right at my kneecap. i bought these for the distressed wash, and will probably return this pair as it looks darker and less faded in person. i look nothing like the model does in this pair of jeans. 


This is the actual review:
 Really wanted to like this top. the model has it on backwards - the "v" is actually the back and closes with an eye hook (that is where the tag is ? there's also a seam running down the back). the front is actually a high round neck. the material has a metallic thread and felt rather scratchy cheap. i'm usually a s or xs. in this top the s had too much material at the bottom and flared out strangely. the xs was too short in the front and moving your arms caused it to ride up. 


This is the actual review:
 I'm about 130 lbs. and 6' tall and usually a small would be my size. these fit, but i would be more comfy in a m. they are stretchy, soft and opaque. perfect for under a couple sweater dresses i picked up by retailer!!! will be re-ordering. 


This is the actual review:
 This sweater is great i bought the black version on sale. my complaint is the pulling factor though. you don't expect retailer sweaters to pill after a couple wears. overall i love the fit and look though so i continue to overlook it and use my sweater comb as needed! 


This is the actual review:
 This t is a beautiful color, and i wish it had worked. it is quite flimsy, however - very thin cotton fabric, without the nice drape that would have made this look good on. i also think it should have been cut a little longer; it flares out right at the hips,so instead of covering them, it highlights them. i'm disappointed, and am returning it. 


This is the actual review:
 I loved the way it looks on. fit tts. only problem for me was it was so itchy. there is that small amt. of wool in it. if you have any problem with wool this may not work for you 


This is the actual review:
 I like this dress very much. the colors are great and the style cute and casual. its perfect with a pair of cognac riding boots and tights.
it really is depicted well in the model photo, although i think it may be cut somewhat shorter than it appears in the photo. i am 5'9" and its shorter on me than on the model who, i imagine, is taller than me.
the dress is made of a substantial, tight knit material. i found it to be a bit boxy so i washed and tumble dried it, hoping to tighten it up a bit. t 


This is the actual review:
 This dress is beautiful. swingy and fun, and the floral pattern is just stunning for fall and winter. i am planning on wearing it with black tights and ankle booties. could be dressed up or down- very versitle. fits my tall curvy frame perfectly. love. 


This is the actual review:
 I got a m in this top and was a little scared but i absolutly love this top, i was debating on it and then caught it on the 30% off sale, my first retailer purchase and it definitely won't be my last! great quality, so comfortable, and very cute! 


This is the actual review:
 It's cute. it works with jeans, linen pants, flip flops. but i wouldn't say it is a "must have". but i'm keeping it because it works! 


This is the actual review:
 What a sweet shirt!! the ski pattern is adorable and they have a cute cat pattern that i've only seen so far in the store. the fit is small. i sized up from my normal 8 to a 10 but could have even gone to a 12 as there wasn't much difference between the 10 and the 12 except in length. 


This is the actual review:
 This is a gorgeous sweater but huge. and when i say huge, i mean gigantic. i'm usually a size large and i don't even think the small would be small enough. the cowl neck and fabric overall is great (although stretches out, which is why it becomes so overwhelming) but the sweater just hung on me like a short blanket. the armholes are very big - deceiving on the model. returning. 


This is the actual review:
 I am a pretty consistent size 2 or 26. ordered these in my usual 26 and, yikes, right out of the package knew they''d be way to small. they have a lot of stretch in them so i was able to get them buttoned but they were wayyyy too small and looked awful. i returned them for a 27 which i am still waiting for. i'll update this review when they arrive. other than the size issue, they were just as pictured/expected. 


This is the actual review:
 I love this dress. the fabric is butter soft, the strapless top stays in place and it hangs beautifully. it is such a wonderfully comfortable summer dress. as previously stated, i'm in love. 


This is the actual review:
 Cute top. a little delicate. my top arrived with a rip in the sleeve. fortunately, my tailor was able to fix it. 


This is the actual review:
 I bought the color "black motif"although the markings look more dark purple and are similar to tie dyed coloring which is one of the things i love about this top. the fabric is so soft and i love the way the neckline falls. it is slightly sheer so may want to wear camisole underneath. personally i'm fine without it. looks great with jeans. i also tried it on in the "blue motif" which is the stripe version but found nothing special about that one. 


This is the actual review:
 I am not particularly large but i have thighs! this pair of pants was totally unflattering! my butt looked fine but everything else looked bigger and my legs looked like stumps. i now understand why it's on sale. you may like these pants if you are tall and thin legged. but otherwise skip them. plus the material seems like it would discolor or stain easily. i had a pair from retailer in the same material last year that developed an odd discoloration the first time i wore them. 


This is the actual review:
 It's comfortable and easy to wear with lots of styles. 


This is the actual review:
 Really interesting, beautiful colors. i found it to be short, even in the regular size (and i'm 5'1 with a short torso). wish the fit was slimmer, but i could see it working with jeans, or being stunning with a black ball skirt. 


This is the actual review:
 I love this dress! the design and colors used are beautifully done. it looks stunning on. can be worn for work or dressed up. 


This is the actual review:
 I tried these on in store and purchased them in the chocolate brown as the fit was perfect! i am 5' 8" and weigh 145 pounds with an approximate 10" difference between hips and waist. the size 27 fit perfectly. i was surprised that they did not wrinkle terribly as they are linen. they withstood wear during a very active day and still looked great by evening. they wash well and only needed a quick touch up with the iron. i liked them so well that i ordered a second pair in grey! 


This is the actual review:
 This is such a cute dress! the skirt is very full but does not add visual weight to those of us who already have enough actual weight. i ordered the black/beige print and the fabric is beautiful. it really drapes and hangs nicely. because there is so much fabric, i don't think this dress needs a lining. it seems to wash and hang dry well. i'm 5'4", about 160 lbs. i like the way the medium fit. the length was slightly above the knee, which was ok with me (i'm nearly 60.) all in all, i can see mys 


This is the actual review:
 This dress is so beautifully embroidered with really cool mix of colors. it is pricey but i do think it will stand the test of time and become an retailer vintage piece. planning to wear it now with wedges and in the fall with booties and a light sweater. 


This is the actual review:
 Was enchanted by this dress from the moment i saw it online. i was able to find it at my local retailer 


This is the actual review:
 I bought this top and it ran huge, i had to get an extra small. well then i wash it according to the instructions and it shrunk horribly! like its so small now my 3 yr old could probably wear it! i'm so sad, i really liked this top....but for it to shrink after one wear is not acceptable. 


This is the actual review:
 Great cardigan that can be put over everything! cute with yoga pants, jeans, or something dressier. 


This is the actual review:
 I bought this on a whim because the colors were so me and fell in love with the bows and button back. i wore this over a lace peplum and opened up the bottom buttons of the button back for a fun layered look. true to size, as i was in my normal size small (5'5", 135lbs, 34c). definitely check this out in person. 


This is the actual review:
 I bought this tunic to wear as a swim cover-up, last minute. but it has become my go to for wearing over skinny jeans. the length makes it so versatile, great layering piece. 


This is the actual review:
 What a cute top! it's a little on the short side for me (5'7") but i'm keeping it because it is so pretty. the lace is lightweight and delicate but seems like it will hold up pretty well. trying to decide what to wear under it, but i think it could look cute with a cami for work, or even a cute bra that has some coverage for super casual days. the lace is completely sheer but doesn't feel as revealing as i would expect. would be super cute over a swim top with jean shorts and sandals...wish i li 


This is the actual review:
 This is a great jacket for fall layering. love the color and cut. it wasn't appearing to me in the photo that the sleeves were knit, but it actually works. i wear the sleeves pushed up as i have really long arms and they are a smidge short. now my go too for casual work days and weekend wear. 


This is the actual review:
 I was hoping not to like this tee so that i can be $48 richer (lol). it's very cute. i would say runs true, but more on the smaller side. i usually wear xs and it's a good cut for me. it's not boxy at all. i love the strap detail on this tee. i hope the quality will hold up. for reference i am 5'3 and 123lbs (2/4). 


This is the actual review:
 This is a cute top, but not worth the price in my opinion. i love the colors, pattern and soft material used. however, the fabric is quite thin and just not worth paying $58. 


This is the actual review:
 This dress does fit small.. i am generally a size 10-12 in maeve but purchased this in a 14. i have a large chest (34g) which contributed to my need to size up. the green color is beautiful and the dress looks darling on. i have a little trouble with the zipper but nothing that required me to return the dress. my biggest complaint is that the straps are a tiny bit too long and don't lie flat on my shoulders. other than that i receive a ton of compliments every time i wear it! 


This is the actual review:
 Adorable jacket. i ordered the medium (5'4, 135 lbs). sleeves are long and have to roll them up. ordering the petite medium to see if it fits better. pink color is great. 


This is the actual review:
 This one didn't work for me. ordered this and the riverdeck tee as similar vein. riverdeck was cute and flattering cut. this one was boxy on me and didn't work. a bit too wide so sending back. 


This is the actual review:
 So i tried this on in the store and ended up with the size 8 (almost could have gone with a size 6) even though i'm 135lbs 5'6" and 34 c. the dress is beautiful! the color is exactly as pictured - brilliant red. the linen is nice and summery. it does make it billow just a tad in the back because of the fabric but not anything to complain about. if i could change just one thing on this dress it would be the length. it just feels a little short for being such a classic and classy dress; just needs 


This is the actual review:
 I ordered the teal color - it is a beautiful color. the fit is a bit odd like one other person commented, but the pros outweigh the cons. i am thinking of ordering the other colors too. 


This is the actual review:
 My conscience screamed at me not to buy this sweater and i wish i had listened! i noted the fact that it was acrylic and also the fact that it was oversized. but i also loved the design and can't resist anything with mustard yellow. i was instantly disappointed when i received it, almost didn't even try it on. how can they possibly charge $185 for terrible itchy 80's acrylic? it looks cheap, it feels cheap and it didn't even fit well. only redeeming quality: easily returnable! 


This is the actual review:
 I ordered this dress in a size 12. i'm 5'9'' and 180lb. i usually wear size 14 but the 12 fit beautifully. the material has a nice stretch to it so that i think it would still fit whether i gained or loss 10 lbs. when i pulled it out of the box i immediately thought it read "grandma", but was pleasantly surprised when i put it on. it is snug in the waist and flares out nicely from there. it hits me on the knee exactly as it does the model in the picture. i plan on wearing this maybe for christma 


This is the actual review:
 I purchased this in black. as in the other reviews the lace detail is a little snug in the arms but it did stretch. i did not have a problem with any fraying or quality issues. the shape is very flattering. i've been asked where i bought this several times. 


This is the actual review:
 I recommend with reservations. these leggings are 100% wool, soft, not itchy, light weight, lovely knitted pattern. they run large. i sized down to a small. the first time i wore them they were fine. but the second time the waist stretched out and was useless: they were falling down as i walked. such a shame because everything else about them is perfect for cold winter days. they even fit under my silk skirt and would make other lighter garments wearable in winter. so what to do? at the price th 


This is the actual review:
 I wasn't expecting much out of this when i was in the store, but liked the colors and thought i'd try it out. grabbed a s and xs (i'm usually a s in everything but maeve is weird sometimes) - the s fit perfectly (tts), and the xs felt tight in the shoulder seam as well as in the armpit. it does have a detachable slip/liner that may be too fitted for some of you are prone to that issue. in the model shot i think the horizontal seam at the swing portion looks higher than it was on me...falling low 


This is the actual review:
 I was so bummed when this came in...the colors and print are beautiful, but the neck is super itchy because of an elastic/ruffle combo. there is also a liner which is a nice touch, but it's significantly shorter than the top layer which drove me nuts. definitely going back. 


This is the actual review:
 Very intricate detail with stitching. love the contrast of black on cream. has slits on each side. bought it for holiday parties! just what i had hoped it would be. 


This is the actual review:
 Awesomely different top. the first time i wore it i didn't wash it so it was big on me. it shrunk from washing so it fits better now. there is some unraveling of the frayed edges though.

zero_shot_results_df.head(20)

import json
import re

def parse_llm_output(raw_output_str):
    # The LLM output is wrapped in ```json\n...\n```, so we need to extract the inner JSON string
    match = re.search(r'```json\n(.*)```', raw_output_str, re.DOTALL)
    if match:
        json_str = match.group(1).strip()
        try:
            return json.loads(json_str)
        except json.JSONDecodeError:
            print(f"JSON Decode Error for: {json_str[:100]}...")
            return None
    return None

# Apply the parsing function to the 'raw_llm_output' column
zero_shot_results_df['parsed_output'] = zero_shot_results_df['raw_llm_output'].apply(parse_llm_output)

# Extract individual fields into new columns
zero_shot_results_df['extracted_categories'] = zero_shot_results_df['parsed_output'].apply(lambda x: x.get('categories') if x else None)
zero_shot_results_df['extracted_sentiment'] = zero_shot_results_df['parsed_output'].apply(lambda x: x.get('sentiment') if x else None)
zero_shot_results_df['extracted_summary'] = zero_shot_results_df['parsed_output'].apply(lambda x: x.get('summary') if x else None)
zero_shot_results_df['extracted_personalized_message'] = zero_shot_results_df['parsed_output'].apply(lambda x: x.get('personalized_message') if x else None)
zero_shot_results_df['extracted_retail_insight'] = zero_shot_results_df['parsed_output'].apply(lambda x: x.get('retail_insight') if x else None)

# Display the DataFrame with the new columns
display(zero_shot_results_df.head())

SYSTEM_MESSAGE_CS20 = """
You are a large-language-model-as-a-judge for other large-language-models, to
check their accuracy and the quality of their output.

Rules:
- Return an evaluation of the zero-shot model output, by creating a dict with
the following keys: "categories", "sentiment", "summary", "personalized_message",
and "retail_insight".
-create a separate rating for each key/value from the review
on a scale from zero to one. Decimals between zero and one
are allowed. Explain the scale then rate each part.

"""

BASE_MESSAGES_CS20 = [
    {"role": "system", "content": SYSTEM_MESSAGE_CS20},
]

from textblob import TextBlob
def llm_as_judge(model_name: str = "gpt-4o-mini", llm_output_to_judge: str = None, original_user_prompt_string_from_zero_shot: str = None) -> dict:
    """
    Acts as an LLM-as-a-judge to score the quality of model outputs.
    It takes the LLM's generated output string, the original review data dictionary,
    and the exact user prompt string that was sent to the zero-shot model.
    """

    if llm_output_to_judge is None or original_user_prompt_string_from_zero_shot is None:
       return {"score": 0.0, "reason": "Missing input for judging"}

    # Extract the actual review text string for TextBlob from the dictionary
    #original_review_text_for_textblob = original_review_data_dict['review_text']
    #review_index_value = original_review_data_dict['review_index'] # Get the scalar index from the dict

    # Use the pre-formatted user_content string from the zero-shot prompt
    # and add the LLM's output for the judge to evaluate
    user_content_for_judge = (
        f"Original Prompt to Zero-Shot LLM:\n{original_user_prompt_string_from_zero_shot}\n\n"
        f"Zero-Shot LLM Output to Evaluate:\n{llm_output_to_judge}\n\n"
        f"Evaluate the Zero-Shot LLM Output based on the provided Original Prompt.\n"
        f"Ensure your evaluation focuses on the accuracy and completeness of the categories, sentiment, summary, personalized message, and retail insight given the original review text in the prompt."
    )

    instructions = BASE_MESSAGES_CS20 + [{
        "role": "user",
        "content": user_content_for_judge
    }]

    response = client.chat.completions.create(
        model=model_name,
        messages=instructions,
        temperature=0.0,
        max_tokens=600
    )

    judge_raw_output = response.choices[0].message.content.strip()

    # Calculate TextBlob sentiment for the original review text string (from the dictionary)
    #textblob_polarity = TextBlob(original_review_text_for_textblob).sentiment.polarity

    return {"judge_response": judge_raw_output} #, "textblob_polarity": textblob_polarity}

eval = []
for index, row in zero_shot_results_df.iterrows():
  evaluation_result_for_row = llm_as_judge(
    llm_output_to_judge = row['raw_llm_output'],
    original_user_prompt_string_from_zero_shot= row['original_user_content']
  )
  eval.append(json.dumps(evaluation_result_for_row, indent=2))

# Print all evaluations collected
for eval_result in eval:
  print(eval_result)

{
  "judge_response": "To evaluate the zero-shot model output, I will assess each key based on its accuracy and completeness in relation to the original review. The scale for rating will be as follows:\n\n- 0: Completely inaccurate or irrelevant\n- 0.25: Somewhat inaccurate or lacking detail\n- 0.5: Partially accurate but missing key elements\n- 0.75: Mostly accurate with minor issues\n- 1: Completely accurate and comprehensive\n\nNow, I will rate each part of the output:\n\n1. **Categories**:\n   - The categories provided (division, department, class, Mt Category) accurately reflect the information in the original review. The classification of \"General\" for division, \"Bottoms\" for department, and \"Pants\" for class is correct. The addition of \"Apparel\" as a category is also appropriate.\n   - **Rating**: 1.0\n\n2. **Sentiment**:\n   - The sentiment is correctly identified as \"positive,\" which aligns with the reviewer's enthusiastic language and satisfaction with the product.\n   - **Rating**: 1.0\n\n3. **Summary**:\n   - The summary captures the main points of the review, including the reviewer's satisfaction with the fit, the flattering design, and the love-at-first-sight reaction to the color. It also notes the lack of information on durability due to not having washed the pants yet. However, it could be slightly more concise.\n   - **Rating**: 0.75\n\n4. **Personalized Message**:\n   - The personalized message is warm and acknowledges the reviewer's positive experience. It encourages continued satisfaction, which is appropriate. However, it could have included a note about the durability aspect since the reviewer mentioned not having washed them yet.\n   - **Rating**: 0.75\n\n5. **Retail Insight**:\n   - The retail insight suggests providing care instructions and durability information, which is relevant and useful based on the reviewer's comment about not having washed the pants yet. This insight is actionable and aligns well with the review's content.\n   - **Rating**: 1.0\n\nNow, compiling the ratings into a dictionary format:\n\n```json\n{\n  \"categories\": 1.0,\n  \"sentiment\": 1.0,\n  \"summary\": 0.75,\n  \"personalized_message\": 0.75,\n  \"retail_insight\": 1.0\n}\n```"
}
{
  "judge_response": "To evaluate the zero-shot model output, I will assess each key based on its accuracy and completeness in relation to the original review. The scale for rating each key is as follows:\n\n- 0: Completely inaccurate or missing information.\n- 0.25: Some relevant information, but significantly lacking or incorrect.\n- 0.5: Partially accurate, but with notable omissions or errors.\n- 0.75: Mostly accurate with minor issues.\n- 1: Fully accurate and complete.\n\nNow, I will rate each part of the output:\n\n1. **Categories**:\n   - The categories provided (division, department, class, Mt Category) accurately reflect the information in the review. The division is \"General,\" the department is \"Tops,\" and the class is \"Knits,\" which are all correct. The \"Mt Category\" as \"Apparel\" is also appropriate.\n   - **Rating**: 1.0\n\n2. **Sentiment**:\n   - The sentiment is correctly identified as \"positive,\" which aligns with the overall tone of the review. The reviewer expresses love for the product and recommends it, indicating a positive sentiment.\n   - **Rating**: 1.0\n\n3. **Summary**:\n   - The summary captures the main points of the review, including the versatility of the top, the comfort, the option to wear it on or off-the-shoulder, and the recommendation due to the sale. It is concise and accurately reflects the review's content.\n   - **Rating**: 1.0\n\n4. **Personalized Message**:\n   - The personalized message is warm and acknowledges the reviewer's positive experience. It reflects the content of the review well, expressing appreciation for the review and encouraging the enjoyment of the product. However, it could be slightly more tailored by mentioning the specific features the reviewer enjoyed.\n   - **Rating**: 0.75\n\n5. **Retail Insight**:\n   - The retail insight is practical and relevant, suggesting a marketing strategy that highlights the product's versatility for different settings. This aligns well with the review's emphasis on the top's suitability for both casual and work environments.\n   - **Rating**: 1.0\n\nBased on the evaluations, here is the final output:\n\n```json\n{\n  \"categories\": 1.0,\n  \"sentiment\": 1.0,\n  \"summary\": 1.0,\n  \"personalized_message\": 0.75,\n  \"retail_insight\": 1.0\n}\n```"
}
{
  "judge_response": "To evaluate the zero-shot model output, I will assess each key based on its accuracy and completeness in relation to the original review. The scale for rating will be as follows:\n\n- 0: Completely inaccurate or irrelevant\n- 0.25: Somewhat inaccurate or missing key elements\n- 0.5: Partially accurate but lacks detail or clarity\n- 0.75: Mostly accurate with minor issues\n- 1: Completely accurate and comprehensive\n\nNow, I will evaluate each part of the output:\n\n1. **Categories**:\n   - The categories provided (division, department, class, Mt Category) are accurate and align with the original review. The addition of \"Mt Category\" as \"Outerwear\" is a reasonable interpretation given that the review mentions a sweater/coat.\n   - **Rating**: 1.0\n\n2. **Sentiment**:\n   - The sentiment is correctly identified as \"positive.\" The review expresses satisfaction with the sweater, mentioning love for its look and the great deal.\n   - **Rating**: 1.0\n\n3. **Summary**:\n   - The summary captures the main points of the review, including the reviewer's love for the sweater, the great deal, and the appropriate weight for chilly weather. It also notes the wish for cooler weather, which is a key aspect of the review.\n   - **Rating**: 1.0\n\n4. **Personalized Message**:\n   - The personalized message is warm and acknowledges the reviewer's positive experience. It effectively reflects the sentiment of the review and encourages future use of the sweater.\n   - **Rating**: 1.0\n\n5. **Retail Insight**:\n   - The retail insight is practical and relevant, suggesting a marketing strategy that highlights the sweater's versatility and features. This aligns well with the review's positive aspects and could attract more customers.\n   - **Rating**: 1.0\n\nBased on the evaluations, here is the final output:\n\n```json\n{\n  \"categories\": 1.0,\n  \"sentiment\": 1.0,\n  \"summary\": 1.0,\n  \"personalized_message\": 1.0,\n  \"retail_insight\": 1.0\n}\n```"
}
{
  "judge_response": "To evaluate the zero-shot model output, I will assess each key based on its accuracy and completeness in relation to the original review. The scale for rating each key is as follows:\n\n- 0: Completely inaccurate or irrelevant\n- 0.1 - 0.4: Somewhat accurate but missing key elements or contains errors\n- 0.5 - 0.7: Mostly accurate with minor issues or omissions\n- 0.8 - 1: Highly accurate and complete\n\nNow, I will evaluate each part of the output:\n\n1. **Categories**:\n   - The categories provided (division, department, class, Mt Category) accurately reflect the information in the original review. The division is \"General,\" the department is \"Bottoms,\" and the class is \"Jeans,\" which are all correct. The \"Mt Category\" as \"Apparel\" is also appropriate.\n   - **Rating**: 1.0\n\n2. **Sentiment**:\n   - The sentiment is correctly identified as \"positive,\" which aligns with the reviewer's enthusiastic language (\"I actually love these jeans\").\n   - **Rating**: 1.0\n\n3. **Summary**:\n   - The summary captures the main points of the review, including the love for the jeans, the true-to-size fit, the unique button detailing, and the versatility for different occasions. It accurately reflects the review's content.\n   - **Rating**: 1.0\n\n4. **Personalized Message**:\n   - The personalized message is warm and acknowledges the reviewer's positive feedback. It expresses appreciation for the review and emphasizes customer satisfaction, which is appropriate for a retail context.\n   - **Rating**: 1.0\n\n5. **Retail Insight**:\n   - The retail insight suggests highlighting the unique features of the jeans, which is a relevant and actionable recommendation based on the review. It aligns well with the reviewer's emphasis on the unique button detailing.\n   - **Rating**: 1.0\n\nOverall, the zero-shot model output is accurate and complete across all evaluated keys. Here is the final evaluation in dictionary format:\n\n```json\n{\n  \"categories\": 1.0,\n  \"sentiment\": 1.0,\n  \"summary\": 1.0,\n  \"personalized_message\": 1.0,\n  \"retail_insight\": 1.0\n}\n```"
}
{
  "judge_response": "To evaluate the zero-shot model output, I will assess each key based on its accuracy and completeness in relation to the original review. The scale for rating is as follows:\n\n- 0: Completely inaccurate or irrelevant\n- 0.25: Somewhat inaccurate or missing key elements\n- 0.5: Partially accurate but lacks depth or clarity\n- 0.75: Mostly accurate with minor issues\n- 1: Completely accurate and comprehensive\n\nNow, I will rate each part of the output:\n\n1. **Categories**:\n   - The categories provided (division, department, class, Mt Category) are accurate and align with the review context. The classification as \"Apparel\" is appropriate given the context of dresses.\n   - **Rating**: 1.0\n\n2. **Sentiment**:\n   - The sentiment is correctly identified as \"positive,\" reflecting the reviewer's enthusiastic tone and satisfaction with the dress.\n   - **Rating**: 1.0\n\n3. **Summary**:\n   - The summary captures the essence of the review well, noting the appreciation for design, fabric, fit, versatility, and suitability for petite sizes. It is concise and accurately reflects the review's main points.\n   - **Rating**: 1.0\n\n4. **Personalized Message**:\n   - The personalized message is warm and acknowledges the reviewer's feedback effectively. It expresses gratitude and highlights the importance of the insights shared, which is appropriate for customer engagement.\n   - **Rating**: 1.0\n\n5. **Retail Insight**:\n   - The retail insight is relevant and actionable, suggesting an expansion of petite sizes and playful prints, which aligns with the reviewer's experience and preferences. It addresses a potential gap in the market based on the review.\n   - **Rating**: 1.0\n\nOverall, the zero-shot model output is accurate and comprehensive across all evaluated categories. Here is the final evaluation in dictionary format:\n\n```json\n{\n  \"categories\": 1.0,\n  \"sentiment\": 1.0,\n  \"summary\": 1.0,\n  \"personalized_message\": 1.0,\n  \"retail_insight\": 1.0\n}\n```"
}
{
  "judge_response": "To evaluate the zero-shot model output, I will assess each key based on its accuracy and relevance to the original review. The scale for rating will be as follows:\n\n- 0: Completely inaccurate or irrelevant\n- 0.1 - 0.4: Somewhat inaccurate or lacking in relevance\n- 0.5: Neutral or average accuracy\n- 0.6 - 0.9: Mostly accurate and relevant, with minor issues\n- 1: Completely accurate and highly relevant\n\nNow, I will rate each part of the output:\n\n1. **Categories**:\n   - The categories provided (division, department, class, Mt Category) accurately reflect the information in the original review. The division \"General Petite,\" department \"Bottoms,\" and class \"Jeans\" are all correct. The \"Mt Category\" as \"Apparel\" is also appropriate.\n   - **Rating**: 1.0\n\n2. **Sentiment**:\n   - The sentiment is correctly identified as \"negative,\" which aligns with the reviewer's disappointment regarding the fit and color of the jeans.\n   - **Rating**: 1.0\n\n3. **Summary**:\n   - The summary captures the main points of the review, including the fit being more like jeggings, disappointment with the color, and the issue with the rip placement. It effectively summarizes the customer's experience.\n   - **Rating**: 1.0\n\n4. **Personalized Message**:\n   - The personalized message is appropriate and empathetic, acknowledging the customer's feedback and expressing regret that the product did not meet expectations. It also indicates that the feedback will be considered for future improvements, which is a good customer service approach.\n   - **Rating**: 1.0\n\n5. **Retail Insight**:\n   - The retail insight suggests providing more accurate product images and detailed descriptions, which is a relevant and constructive suggestion based on the reviewer's comments about the color and fit discrepancies. This insight is actionable and aligns well with the issues raised in the review.\n   - **Rating**: 1.0\n\nBased on the evaluations, here is the final output:\n\n```json\n{\n  \"categories\": 1.0,\n  \"sentiment\": 1.0,\n  \"summary\": 1.0,\n  \"personalized_message\": 1.0,\n  \"retail_insight\": 1.0\n}\n```"
}
{
  "judge_response": "To evaluate the zero-shot model output, I will assess each key based on its accuracy and relevance to the original review. The scale for rating is as follows:\n\n- 0: Completely inaccurate or irrelevant\n- 0.25: Somewhat inaccurate or lacks detail\n- 0.5: Moderately accurate but could be improved\n- 0.75: Mostly accurate with minor issues\n- 1: Completely accurate and comprehensive\n\nNow, I will rate each part of the output:\n\n1. **Categories**:\n   - The categories provided (division, department, class, Mt Category) accurately reflect the information in the original review. The division is \"General,\" the department is \"Tops,\" and the class is \"Blouses,\" which are all correct. The \"Mt Category\" as \"Apparel\" is also appropriate.\n   - **Rating**: 1.0\n\n2. **Sentiment**:\n   - The sentiment is labeled as \"negative,\" which aligns well with the review's overall tone of disappointment and dissatisfaction with the product.\n   - **Rating**: 1.0\n\n3. **Summary**:\n   - The summary captures the main points of the review, including disappointment with fit, material quality, and design confusion. It effectively conveys the essence of the review without omitting critical details.\n   - **Rating**: 1.0\n\n4. **Personalized Message**:\n   - The personalized message is empathetic and acknowledges the customer's feedback. It also indicates that the insights will be used to improve product descriptions and sizing guidance, which is a positive response to the review.\n   - **Rating**: 1.0\n\n5. **Retail Insight**:\n   - The retail insight provides actionable suggestions, such as improving product images and descriptions and offering a more detailed sizing guide. These suggestions are relevant and directly address the issues raised in the review.\n   - **Rating**: 1.0\n\nOverall, the zero-shot model output is accurate and comprehensive across all evaluated categories. Here is the final evaluation in dictionary format:\n\n```json\n{\n  \"categories\": 1.0,\n  \"sentiment\": 1.0,\n  \"summary\": 1.0,\n  \"personalized_message\": 1.0,\n  \"retail_insight\": 1.0\n}\n```"
}
{
  "judge_response": "To evaluate the zero-shot model output, I will assess each key based on its accuracy and relevance to the original review. The scale for rating is as follows:\n\n- 0: Completely inaccurate or irrelevant\n- 0.1 - 0.4: Somewhat inaccurate or lacking in relevance\n- 0.5: Neutral; neither accurate nor inaccurate\n- 0.6 - 0.9: Mostly accurate with minor issues\n- 1: Completely accurate and relevant\n\nNow, I will rate each part of the output:\n\n1. **Categories**:\n   - The categories provided (division, department, class, Mt Category) accurately reflect the information in the original review. The division \"Intimates,\" department \"Intimate,\" and class \"Legwear\" are all correct. The \"Mt Category\" being \"Legwear\" is also appropriate.\n   - **Rating**: 1.0\n\n2. **Sentiment**:\n   - The sentiment is labeled as \"positive,\" which aligns with the reviewer's overall positive experience with the product, noting its qualities and intent to reorder.\n   - **Rating**: 1.0\n\n3. **Summary**:\n   - The summary captures the key points of the review, mentioning the qualities of the legwear (stretchy, soft, opaque) and the suggestion for a larger size for comfort. It accurately reflects the reviewer's sentiments and main points.\n   - **Rating**: 1.0\n\n4. **Personalized Message**:\n   - The personalized message is appropriate and acknowledges the reviewer's feedback positively. It thanks the reviewer and addresses the points made about softness and opacity, while also noting the sizing feedback for future improvements. This is a good customer service response.\n   - **Rating**: 1.0\n\n5. **Retail Insight**:\n   - The retail insight suggests offering a size guide that includes customer feedback on fit, which is a relevant and actionable suggestion based on the reviewer's comment about sizing. This insight is practical and aligns well with the review's content.\n   - **Rating**: 1.0\n\nOverall, the zero-shot model output is accurate and relevant across all categories, sentiment, summary, personalized message, and retail insight. \n\nFinal Evaluation:\n```json\n{\n  \"categories\": 1.0,\n  \"sentiment\": 1.0,\n  \"summary\": 1.0,\n  \"personalized_message\": 1.0,\n  \"retail_insight\": 1.0\n}\n```"
}
{
  "judge_response": "To evaluate the zero-shot model output, I will assess each key based on its accuracy and completeness in relation to the original review. The scale for rating each key is as follows:\n\n- 0: Completely inaccurate or missing\n- 0.25: Somewhat inaccurate or incomplete\n- 0.5: Moderately accurate but with notable issues\n- 0.75: Mostly accurate with minor issues\n- 1: Completely accurate and comprehensive\n\nNow, I will rate each part of the output:\n\n1. **Categories**:\n   - The categories provided (division, department, class, Mt Category) are accurate and align with the original review. The \"Mt Category\" of \"Sweaters\" is a reasonable addition based on the context of the review.\n   - **Rating**: 1.0\n\n2. **Sentiment**:\n   - The sentiment is labeled as \"neutral,\" which does not accurately reflect the review's overall tone. The reviewer expresses both appreciation for the fit and look of the sweater and disappointment regarding the pilling issue, indicating a mixed sentiment rather than neutral.\n   - **Rating**: 0.25\n\n3. **Summary**:\n   - The summary captures the main points of the review well, noting the customer's appreciation for the fit and look while also mentioning the disappointment with pilling. It is concise and accurately reflects the review's content.\n   - **Rating**: 1.0\n\n4. **Personalized Message**:\n   - The personalized message is well-crafted, acknowledging the customer's feedback and expressing understanding of their frustration with pilling. It also thanks the customer, which is a positive touch. However, it could have included a more direct acknowledgment of the specific issue raised (pilling) rather than just a general statement about frustration.\n   - **Rating**: 0.75\n\n5. **Retail Insight**:\n   - The retail insight is practical and relevant, suggesting improvements in fabric quality and offering care tips. It addresses a common concern among customers, which is valuable for the retailer. However, it could have been more specific about the type of fabric or care tips that might help reduce pilling.\n   - **Rating**: 0.75\n\nNow, compiling the ratings into a dictionary format:\n\n```json\n{\n  \"categories\": 1.0,\n  \"sentiment\": 0.25,\n  \"summary\": 1.0,\n  \"personalized_message\": 0.75,\n  \"retail_insight\": 0.75\n}\n```"
}
{
  "judge_response": "To evaluate the zero-shot model output, I will assess each key based on its accuracy and completeness in relation to the original review. The scale for rating will be as follows:\n\n- 0: Completely inaccurate or irrelevant\n- 0.25: Somewhat inaccurate or missing key elements\n- 0.5: Partially accurate but lacks detail or clarity\n- 0.75: Mostly accurate with minor issues\n- 1: Completely accurate and comprehensive\n\nNow, I will rate each part of the output:\n\n1. **Categories**:\n   - The categories provided (division, department, class, and Mt Category) accurately reflect the information in the original review. The division \"General Petite,\" department \"Tops,\" and class \"Knits\" are all correct. The addition of \"Mt Category\" as \"Apparel\" is also appropriate, as it encompasses the product type.\n   - **Rating**: 1.0\n\n2. **Sentiment**:\n   - The sentiment is correctly identified as \"negative,\" which aligns with the reviewer's disappointment regarding the flimsy fabric and fit of the t-shirt.\n   - **Rating**: 1.0\n\n3. **Summary**:\n   - The summary captures the main points of the review: the beautiful color, disappointment with the flimsy fabric, and the return decision. It effectively conveys the essence of the review without omitting any critical details.\n   - **Rating**: 1.0\n\n4. **Personalized Message**:\n   - The personalized message is appropriate and empathetic, acknowledging the customer's feedback and expressing regret that the product did not meet expectations. It also emphasizes the value of customer insights for product improvement, which is a good practice in customer service.\n   - **Rating**: 1.0\n\n5. **Retail Insight**:\n   - The retail insight provides actionable recommendations based on the review. It suggests enhancing fabric quality and adjusting the cut for better fit, which directly addresses the customer's concerns. The mention of a preference for longer cuts is also relevant and insightful.\n   - **Rating**: 1.0\n\nOverall, the zero-shot model output is accurate and comprehensive across all evaluated categories. \n\nFinal Evaluation:\n```json\n{\n  \"categories\": 1.0,\n  \"sentiment\": 1.0,\n  \"summary\": 1.0,\n  \"personalized_message\": 1.0,\n  \"retail_insight\": 1.0\n}\n```"
}
{
  "judge_response": "To evaluate the Zero-Shot LLM output, I will assess each key based on its accuracy and completeness in relation to the original review provided. The scale for rating each key is as follows:\n\n- 0: Completely inaccurate or missing\n- 0.25: Somewhat inaccurate or incomplete\n- 0.5: Moderately accurate but with notable issues\n- 0.75: Mostly accurate with minor issues\n- 1: Completely accurate and comprehensive\n\nNow, I will evaluate each part of the output:\n\n1. **Categories**:\n   - The categories provided (division, department, class, and Mt Category) accurately reflect the information from the original prompt. The \"Mt Category\" of \"Apparel\" is a reasonable addition, as it encompasses the other categories.\n   - **Rating**: 1.0\n\n2. **Sentiment**:\n   - The sentiment is labeled as \"neutral,\" which does not accurately capture the mixed feelings expressed in the review. The reviewer loved the look and fit but mentioned a significant issue with itchiness. A more appropriate sentiment would be \"mixed\" or \"negative\" due to the complaint about itchiness.\n   - **Rating**: 0.25\n\n3. **Summary**:\n   - The summary accurately reflects the positive aspects of the product (great look and true to size) and correctly identifies the issue with itchiness due to wool content. It captures the essence of the review well.\n   - **Rating**: 1.0\n\n4. **Personalized Message**:\n   - The personalized message is well-crafted, acknowledging the positive feedback while also addressing the concern about itchiness. It shows appreciation for the feedback and indicates a willingness to consider improvements, which is appropriate for customer engagement.\n   - **Rating**: 1.0\n\n5. **Retail Insight**:\n   - The retail insight provides a constructive suggestion to offer a wool-free alternative or more detailed fabric information, which is relevant and actionable based on the reviewer's concern. This insight is valuable for improving customer satisfaction.\n   - **Rating**: 1.0\n\nBased on the evaluations, here is the final output:\n\n```json\n{\n  \"categories\": 1.0,\n  \"sentiment\": 0.25,\n  \"summary\": 1.0,\n  \"personalized_message\": 1.0,\n  \"retail_insight\": 1.0\n}\n```"
}
{
  "judge_response": "To evaluate the zero-shot model output, I will assess each key based on its accuracy and completeness in relation to the original review. The scale for rating each key is as follows:\n\n- 0: Completely inaccurate or irrelevant\n- 0.1 - 0.4: Somewhat accurate but missing key details or contains errors\n- 0.5 - 0.7: Mostly accurate with minor issues or omissions\n- 0.8 - 1: Fully accurate and comprehensive\n\nNow, I will rate each part of the output:\n\n1. **Categories**:\n   - The categories provided (division, department, class, Mt Category) are accurate and align with the original review. The \"Mt Category\" as \"Apparel\" is a reasonable generalization.\n   - **Rating**: 1.0\n\n2. **Sentiment**:\n   - The sentiment is correctly identified as \"positive,\" reflecting the reviewer's overall enjoyment of the dress.\n   - **Rating**: 1.0\n\n3. **Summary**:\n   - The summary captures the main points of the review, including the appreciation for colors and style, as well as the notes on fit. However, it could mention the washing and drying process that the reviewer undertook to address the boxiness.\n   - **Rating**: 0.8\n\n4. **Personalized Message**:\n   - The personalized message is appropriate and acknowledges the reviewer's feedback positively. It also addresses the insights about fit, which is relevant.\n   - **Rating**: 1.0\n\n5. **Retail Insight**:\n   - The retail insight is practical and directly addresses the reviewer's concern about fit based on height. It suggests a useful action for the retailer to improve customer experience.\n   - **Rating**: 1.0\n\nBased on the evaluations, here is the final output:\n\n```json\n{\n  \"categories\": 1.0,\n  \"sentiment\": 1.0,\n  \"summary\": 0.8,\n  \"personalized_message\": 1.0,\n  \"retail_insight\": 1.0\n}\n```"
}
{
  "judge_response": "To evaluate the zero-shot model output, I will assess each key based on its accuracy and completeness in relation to the original review. The scale for rating each key is as follows:\n\n- 0: Completely inaccurate or irrelevant\n- 0.1 - 0.4: Some accuracy but significant issues or omissions\n- 0.5 - 0.7: Mostly accurate with minor issues\n- 0.8 - 1: Highly accurate and complete\n\nNow, I will evaluate each part of the output:\n\n1. **Categories**:\n   - The categories provided (division, department, class, Mt Category) accurately reflect the context of the review, which is about a dress. The \"Mt Category\" as \"Apparel\" is also appropriate. \n   - **Rating**: 1.0\n\n2. **Sentiment**:\n   - The sentiment is correctly identified as \"positive,\" which aligns with the reviewer's enthusiastic language and overall satisfaction with the dress.\n   - **Rating**: 1.0\n\n3. **Summary**:\n   - The summary captures the main points of the review, including the beauty, versatility, and fit of the dress. It accurately reflects the reviewer's sentiments and key details.\n   - **Rating**: 1.0\n\n4. **Personalized Message**:\n   - The personalized message is warm and acknowledges the reviewer's positive experience. It encourages further engagement, which is appropriate for customer interaction. However, it could have included a mention of the specific styling options (like black tights and ankle booties) mentioned in the review for a more tailored response.\n   - **Rating**: 0.9\n\n5. **Retail Insight**:\n   - The retail insight is practical and relevant, suggesting to highlight the dress's versatility and consider showcasing styling options for different body types. This aligns well with the review's emphasis on versatility and fit.\n   - **Rating**: 1.0\n\nBased on the evaluations, here is the final output:\n\n```json\n{\n  \"categories\": 1.0,\n  \"sentiment\": 1.0,\n  \"summary\": 1.0,\n  \"personalized_message\": 0.9,\n  \"retail_insight\": 1.0\n}\n```"
}
{
  "judge_response": "To evaluate the zero-shot model output, I will assess each key based on its accuracy and completeness in relation to the original review. The scale for rating each key is as follows:\n\n- 0: Completely inaccurate or irrelevant\n- 0.25: Somewhat inaccurate or missing key elements\n- 0.5: Partially accurate but lacks detail or clarity\n- 0.75: Mostly accurate with minor issues\n- 1: Completely accurate and comprehensive\n\nNow, I will evaluate each part of the output:\n\n1. **Categories**:\n   - The categories provided (division, department, class, Mt Category) accurately reflect the information in the original prompt. The division \"General Petite,\" department \"Tops,\" and class \"Knits\" are all correct. The addition of \"Mt Category\" as \"Apparel\" is also appropriate as it encompasses the other categories.\n   - **Rating**: 1\n\n2. **Sentiment**:\n   - The sentiment is correctly identified as \"positive.\" The review expresses enthusiasm and satisfaction with the product, which aligns with the sentiment classification.\n   - **Rating**: 1\n\n3. **Summary**:\n   - The summary accurately captures the essence of the review. It mentions the customer's thrill with the purchase, praises for quality, comfort, and style, and notes the significance of the sale. It effectively summarizes the key points without omitting any critical information.\n   - **Rating**: 1\n\n4. **Personalized Message**:\n   - The personalized message is warm and appreciative, reflecting the positive sentiment of the review. It acknowledges the customer's feedback and expresses a desire for future business, which is appropriate for a retail context. However, it could be slightly more tailored by mentioning the specific product (the top) to enhance personalization.\n   - **Rating**: 0.75\n\n5. **Retail Insight**:\n   - The retail insight is relevant and actionable, suggesting that promoting sales and discounts can positively impact customer decisions and satisfaction. This aligns well with the review's mention of the customer being influenced by the sale. However, it could be more specific by suggesting how to promote these sales (e.g., through email marketing or social media).\n   - **Rating**: 0.75\n\nNow, I will compile the evaluation into a dictionary format:\n\n```json\n{\n  \"categories\": 1,\n  \"sentiment\": 1,\n  \"summary\": 1,\n  \"personalized_message\": 0.75,\n  \"retail_insight\": 0.75\n}\n```"
}
{
  "judge_response": "To evaluate the zero-shot model output, I will assess each key based on its accuracy and relevance to the original review. The scale for rating will be as follows:\n\n- 0: Completely inaccurate or irrelevant\n- 0.25: Somewhat inaccurate or partially relevant\n- 0.5: Neutral; neither accurate nor inaccurate\n- 0.75: Mostly accurate or relevant, with minor issues\n- 1: Completely accurate and relevant\n\nNow, I will evaluate each part of the output:\n\n1. **Categories**:\n   - The categories provided (division, department, class, and Mt Category) are accurate and align with the original prompt. The \"Mt Category\" of \"Loungewear\" is a reasonable interpretation of the \"Lounge\" class.\n   - **Rating**: 1.0\n\n2. **Sentiment**:\n   - The sentiment is labeled as \"neutral,\" which is appropriate given the review's mixed feelings. The reviewer expresses a positive view of the product being \"cute\" and \"works,\" but also states it is not a \"must have,\" indicating a lack of strong enthusiasm.\n   - **Rating**: 1.0\n\n3. **Summary**:\n   - The summary accurately captures the essence of the review, highlighting that the product is cute and versatile but not essential. It succinctly reflects the reviewer's sentiments.\n   - **Rating**: 1.0\n\n4. **Personalized Message**:\n   - The personalized message is positive and acknowledges the feedback, which is appropriate. However, it could be seen as slightly generic and not tailored specifically to the review's content about the product not being a \"must have.\"\n   - **Rating**: 0.75\n\n5. **Retail Insight**:\n   - The retail insight suggests highlighting the product's versatility in marketing, which is a logical recommendation based on the review. However, it could be more specific about how to address the \"not a must have\" sentiment to better align with the review's overall tone.\n   - **Rating**: 0.75\n\nNow, I will compile the evaluation into a dictionary format:\n\n```json\n{\n  \"categories\": 1.0,\n  \"sentiment\": 1.0,\n  \"summary\": 1.0,\n  \"personalized_message\": 0.75,\n  \"retail_insight\": 0.75\n}\n```"
}
{
  "judge_response": "To evaluate the zero-shot model output, I will assess each key based on its accuracy and completeness in relation to the original review. The scale for rating will be as follows:\n\n- 0: Completely inaccurate or irrelevant\n- 0.25: Somewhat inaccurate or missing key elements\n- 0.5: Partially accurate but lacks detail or clarity\n- 0.75: Mostly accurate with minor issues\n- 1: Completely accurate and comprehensive\n\nNow, I will rate each part of the output:\n\n1. **Categories**:\n   - The categories provided (division, department, class, Mt Category) are accurate and align with the original review. The \"Mt Category\" as \"Apparel\" is a reasonable classification for the context.\n   - **Rating**: 1.0\n\n2. **Sentiment**:\n   - The sentiment is correctly identified as \"positive,\" reflecting the reviewer's enthusiasm for the shirt's patterns and overall satisfaction despite the sizing issue.\n   - **Rating**: 1.0\n\n3. **Summary**:\n   - The summary accurately captures the main points of the review: the reviewer loves the patterns and notes the fit runs small, suggesting sizing up. It is concise and informative.\n   - **Rating**: 1.0\n\n4. **Personalized Message**:\n   - The personalized message is appropriate and acknowledges the reviewer's feedback positively. It expresses appreciation for the patterns and addresses the sizing concern, which is relevant to the review.\n   - **Rating**: 1.0\n\n5. **Retail Insight**:\n   - The retail insight is practical and relevant, suggesting the provision of detailed sizing charts and fit guides. This aligns well with the review's mention of sizing issues and could indeed help improve customer satisfaction.\n   - **Rating**: 1.0\n\nBased on the evaluations, here is the final output:\n\n```json\n{\n  \"categories\": 1.0,\n  \"sentiment\": 1.0,\n  \"summary\": 1.0,\n  \"personalized_message\": 1.0,\n  \"retail_insight\": 1.0\n}\n```"
}
{
  "judge_response": "To evaluate the zero-shot model output, I will assess each key based on its accuracy and completeness in relation to the original review. The scale for rating is as follows:\n\n- 0: Completely inaccurate or irrelevant\n- 0.25: Somewhat inaccurate or lacking detail\n- 0.5: Partially accurate but missing key elements\n- 0.75: Mostly accurate with minor issues\n- 1: Completely accurate and comprehensive\n\nNow, I will rate each part of the output:\n\n1. **Categories**:\n   - The categories provided (division, department, class, Mt Category) are accurate and align with the original review. The inclusion of \"Mt Category\" as \"Apparel\" is appropriate as it encompasses the other categories.\n   - **Rating**: 1.0\n\n2. **Sentiment**:\n   - The sentiment is correctly identified as \"negative,\" which reflects the reviewer's disappointment with the fit of the sweater. The model accurately captures the overall tone of the review.\n   - **Rating**: 1.0\n\n3. **Summary**:\n   - The summary effectively captures the main points of the review, including the excessive size of the sweater and the reviewer's appreciation for the fabric and design. It succinctly conveys the key issues raised by the reviewer.\n   - **Rating**: 1.0\n\n4. **Personalized Message**:\n   - The personalized message is appropriate and empathetic, acknowledging the reviewer's feedback and expressing regret that the product did not meet expectations. It also indicates that the feedback will be considered for future designs, which is a positive touch.\n   - **Rating**: 1.0\n\n5. **Retail Insight**:\n   - The retail insight is practical and relevant, suggesting that the retailer provide more detailed sizing information and fit guides. This addresses the main concern raised in the review about sizing and fit, making it a valuable recommendation.\n   - **Rating**: 1.0\n\nOverall, the zero-shot model output is accurate and comprehensive across all evaluated categories. Here is the final evaluation in dictionary format:\n\n```json\n{\n  \"categories\": 1.0,\n  \"sentiment\": 1.0,\n  \"summary\": 1.0,\n  \"personalized_message\": 1.0,\n  \"retail_insight\": 1.0\n}\n```"
}
{
  "judge_response": "To evaluate the zero-shot model output, I will assess each key based on its accuracy and completeness in relation to the original review provided. The scale for rating will be as follows:\n\n- 0: Completely inaccurate or irrelevant\n- 0.25: Somewhat inaccurate or lacking detail\n- 0.5: Partially accurate but missing key elements\n- 0.75: Mostly accurate with minor issues\n- 1: Completely accurate and comprehensive\n\nNow, I will rate each part of the output:\n\n1. **Categories**:\n   - The categories provided (division, department, class, Mt Category) are accurate and align with the original review. The classification of \"Apparel\" is appropriate for jeans.\n   - **Rating**: 1.0\n\n2. **Sentiment**:\n   - The sentiment is correctly identified as \"negative,\" reflecting the customer's dissatisfaction with the sizing of the jeans.\n   - **Rating**: 1.0\n\n3. **Summary**:\n   - The summary accurately captures the main points of the review, including the sizing issue, the return for a larger size, and the intention to update the review later. It is concise and informative.\n   - **Rating**: 1.0\n\n4. **Personalized Message**:\n   - The personalized message is empathetic and acknowledges the customer's frustration with sizing inconsistencies. It also expresses hope for a better fit with the larger size, which is appropriate and customer-friendly.\n   - **Rating**: 1.0\n\n5. **Retail Insight**:\n   - The retail insight provides practical suggestions, such as offering a detailed sizing guide and a size exchange program, which are relevant to the issues raised in the review. This insight is actionable and could enhance customer satisfaction.\n   - **Rating**: 1.0\n\nBased on the evaluations, here is the final output:\n\n```json\n{\n  \"categories\": 1.0,\n  \"sentiment\": 1.0,\n  \"summary\": 1.0,\n  \"personalized_message\": 1.0,\n  \"retail_insight\": 1.0\n}\n```"
}
{
  "judge_response": "To evaluate the zero-shot model output, I will assess each key based on its accuracy and completeness in relation to the original review. The scale for rating each key is as follows:\n\n- 0: Completely inaccurate or irrelevant\n- 0.25: Somewhat inaccurate or missing key elements\n- 0.5: Partially accurate but lacks detail or clarity\n- 0.75: Mostly accurate with minor issues\n- 1: Completely accurate and comprehensive\n\nNow, I will rate each part of the output:\n\n1. **Categories**:\n   - The categories provided (division, department, class, Mt Category) accurately reflect the information in the original review. The division, department, and class are all correctly identified as \"General Petite\" and \"Dresses.\" The addition of \"Mt Category\" as \"Apparel\" is appropriate and relevant.\n   - **Rating**: 1.0\n\n2. **Sentiment**:\n   - The sentiment is correctly identified as \"positive,\" which aligns with the enthusiastic tone of the review. The reviewer expresses love for the dress, indicating a strong positive sentiment.\n   - **Rating**: 1.0\n\n3. **Summary**:\n   - The summary accurately captures the main points of the review, including the soft fabric, secure strapless design, and overall comfort. It reflects the reviewer's strong affection for the dress, which is essential to convey.\n   - **Rating**: 1.0\n\n4. **Personalized Message**:\n   - The personalized message is appropriate and reflects gratitude for the feedback. It acknowledges the reviewer's love for the dress and its comfort, which aligns well with the review's content. However, it could be slightly more personalized by mentioning specific features like the \"butter soft\" fabric.\n   - **Rating**: 0.75\n\n5. **Retail Insight**:\n   - The retail insight is relevant and actionable, suggesting that the dress's comfort and fabric quality should be highlighted in marketing materials. This aligns with the review's emphasis on these features, making it a valuable suggestion for retailers.\n   - **Rating**: 1.0\n\nBased on the evaluations, here is the final output:\n\n```json\n{\n  \"categories\": 1.0,\n  \"sentiment\": 1.0,\n  \"summary\": 1.0,\n  \"personalized_message\": 0.75,\n  \"retail_insight\": 1.0\n}\n```"
}
{
  "judge_response": "To evaluate the zero-shot model output, I will assess each key based on its accuracy and relevance to the original review. The scale for rating will be as follows:\n\n- 0: Completely inaccurate or irrelevant\n- 0.1 - 0.4: Somewhat inaccurate or lacking in relevance\n- 0.5: Neutral or average quality\n- 0.6 - 0.9: Mostly accurate and relevant, with minor issues\n- 1: Completely accurate and highly relevant\n\nNow, I will evaluate each part of the output:\n\n1. **Categories**:\n   - The categories provided (division, department, class, Mt Category) accurately reflect the information in the original review. The \"Mt Category\" as \"Apparel\" is a reasonable generalization.\n   - **Rating**: 1.0\n\n2. **Sentiment**:\n   - The sentiment is labeled as \"negative,\" which is appropriate given the mention of a rip in the sleeve and the need for a tailor. However, the review also contains a positive note about the top being cute, which could suggest a more mixed sentiment.\n   - **Rating**: 0.7\n\n3. **Summary**:\n   - The summary captures the key points of the review: the cuteness of the top, its delicacy, and the issue with the rip. It effectively conveys the main ideas without omitting important details.\n   - **Rating**: 1.0\n\n4. **Personalized Message**:\n   - The personalized message is empathetic and acknowledges the customer's experience. It expresses gratitude for the feedback and addresses the issue of the rip, which is appropriate and well-crafted.\n   - **Rating**: 1.0\n\n5. **Retail Insight**:\n   - The retail insight suggests improving quality control measures, which is a relevant and constructive recommendation based on the issue raised in the review. It addresses the concern of receiving a defective item.\n   - **Rating**: 1.0\n\nBased on the evaluations, here is the final output:\n\n```json\n{\n  \"categories\": 1.0,\n  \"sentiment\": 0.7,\n  \"summary\": 1.0,\n  \"personalized_message\": 1.0,\n  \"retail_insight\": 1.0\n}\n```"
}
{
  "judge_response": "To evaluate the Zero-Shot LLM output, I will assess each key based on its accuracy and completeness in relation to the original review. The scale for rating each key is as follows:\n\n- 0: Completely inaccurate or irrelevant\n- 0.25: Somewhat inaccurate or missing key elements\n- 0.5: Partially accurate but lacks detail or clarity\n- 0.75: Mostly accurate with minor issues\n- 1: Completely accurate and comprehensive\n\nNow, I will rate each part of the output:\n\n1. **Categories**:\n   - The categories provided (division, department, class, Mt Category) accurately reflect the information in the original review. The classification as \"Apparel\" is appropriate given the context.\n   - **Rating**: 1.0\n\n2. **Sentiment**:\n   - The sentiment is correctly identified as \"positive,\" reflecting the reviewer's overall satisfaction with the \"black motif\" blouse. The mention of the \"blue motif\" being less appealing does not detract from the positive sentiment regarding the first blouse.\n   - **Rating**: 1.0\n\n3. **Summary**:\n   - The summary captures the main points of the review, including the appreciation for the soft fabric and unique color of the \"black motif\" blouse, as well as the less favorable opinion of the \"blue motif.\" It is concise and accurately reflects the review's content.\n   - **Rating**: 1.0\n\n4. **Personalized Message**:\n   - The personalized message is warm and acknowledges the reviewer's feedback. It expresses appreciation for the positive comments about the \"black motif\" blouse and addresses the feedback on the \"blue motif.\" However, it could be slightly more tailored by mentioning the specific aspects the reviewer loved about the \"black motif.\"\n   - **Rating**: 0.75\n\n5. **Retail Insight**:\n   - The retail insight provides actionable suggestions based on the review. It highlights the importance of marketing unique color patterns and suggests styling tips for sheer fabrics, which aligns well with the review's content. This insight is relevant and could help improve customer satisfaction.\n   - **Rating**: 1.0\n\nBased on the evaluations, here is the final output:\n\n```json\n{\n  \"categories\": 1.0,\n  \"sentiment\": 1.0,\n  \"summary\": 1.0,\n  \"personalized_message\": 0.75,\n  \"retail_insight\": 1.0\n}\n```"
}
{
  "judge_response": "To evaluate the zero-shot model output, I will assess each key based on its accuracy and relevance to the original review. The scale for rating is as follows:\n\n- 0: Completely inaccurate or irrelevant\n- 0.25: Somewhat inaccurate or partially relevant\n- 0.5: Moderately accurate but lacking in detail or clarity\n- 0.75: Mostly accurate with minor issues\n- 1: Completely accurate and highly relevant\n\nNow, I will rate each part of the output:\n\n1. **Categories**:\n   - The categories provided (division, department, class, Mt Category) accurately reflect the information in the original review. The classification of \"Pants\" under \"Bottoms\" in the \"General\" division is correct. The addition of \"Mt Category\" as \"Apparel\" is also appropriate.\n   - **Rating**: 1\n\n2. **Sentiment**:\n   - The sentiment is correctly identified as \"negative,\" which aligns with the reviewer's dissatisfaction with the pants. The review expresses clear disappointment and criticism, justifying the negative sentiment classification.\n   - **Rating**: 1\n\n3. **Summary**:\n   - The summary captures the main points of the review, highlighting the unflattering fit for those with larger thighs and concerns about material discoloration. It succinctly conveys the reviewer's experience and opinion.\n   - **Rating**: 1\n\n4. **Personalized Message**:\n   - The personalized message is appropriate and empathetic, acknowledging the reviewer's feedback and expressing regret that the product did not meet expectations. It also indicates that the feedback will be considered for future improvements, which is a positive response.\n   - **Rating**: 1\n\n5. **Retail Insight**:\n   - The retail insight provides actionable suggestions, such as offering a wider range of sizes and including material care instructions. These recommendations are relevant to the issues raised in the review and demonstrate an understanding of customer needs.\n   - **Rating**: 1\n\nOverall, the zero-shot model output is accurate and relevant across all categories. Here is the final evaluation:\n\n```json\n{\n  \"categories\": 1,\n  \"sentiment\": 1,\n  \"summary\": 1,\n  \"personalized_message\": 1,\n  \"retail_insight\": 1\n}\n```"
}
{
  "judge_response": "To evaluate the zero-shot model output, I will assess each key based on its accuracy and completeness in relation to the original review. The scale for rating is as follows:\n\n- 0: Completely inaccurate or irrelevant\n- 0.25: Somewhat inaccurate or missing key elements\n- 0.5: Partially accurate but lacks depth or completeness\n- 0.75: Mostly accurate with minor issues\n- 1: Fully accurate and comprehensive\n\nNow, I will evaluate each part of the output:\n\n1. **Categories**:\n   - The categories provided (division, department, class, and Mt Category) accurately reflect the information in the original prompt. The division is \"General,\" the department is \"Bottoms,\" and the class is \"Skirts,\" which are all correct. The addition of \"Mt Category\" as \"Apparel\" is also appropriate as it encompasses the provided categories.\n   - **Rating**: 1.0\n\n2. **Sentiment**:\n   - The sentiment is labeled as \"positive,\" which accurately reflects the tone of the original review. The review expresses satisfaction with the comfort and versatility of the skirt, which aligns with a positive sentiment.\n   - **Rating**: 1.0\n\n3. **Summary**:\n   - The summary states that \"The skirt is praised for its comfort and versatility in styling.\" This is a concise and accurate representation of the review's content, capturing the key points effectively.\n   - **Rating**: 1.0\n\n4. **Personalized Message**:\n   - The personalized message thanks the reviewer for their positive feedback and acknowledges the comfort and versatility of the skirt. It is warm and engaging, which is appropriate for customer interaction. However, it could be slightly more personalized by referencing the specific style or feature mentioned in the review.\n   - **Rating**: 0.75\n\n5. **Retail Insight**:\n   - The retail insight suggests expanding the range of styles and colors for skirts to enhance customer satisfaction. This is a relevant and actionable suggestion based on the review's mention of \"lots of styles.\" It demonstrates an understanding of customer preferences and market trends.\n   - **Rating**: 1.0\n\nBased on the evaluations, here is the final output:\n\n```json\n{\n  \"categories\": 1.0,\n  \"sentiment\": 1.0,\n  \"summary\": 1.0,\n  \"personalized_message\": 0.75,\n  \"retail_insight\": 1.0\n}\n```"
}
{
  "judge_response": "To evaluate the zero-shot model output, I will assess each key based on its accuracy and relevance to the original review. The scale for rating is as follows:\n\n- 0: Completely inaccurate or irrelevant\n- 0.1 - 0.4: Somewhat inaccurate or lacking in relevance\n- 0.5: Neutral; neither accurate nor inaccurate\n- 0.6 - 0.9: Mostly accurate with minor issues\n- 1: Completely accurate and relevant\n\nNow, I will rate each part of the output:\n\n1. **Categories**:\n   - The categories provided (division, department, class, Mt Category) accurately reflect the information in the original review. The \"Mt Category\" as \"Apparel\" is a reasonable classification for the context.\n   - **Rating**: 1.0\n\n2. **Sentiment**:\n   - The sentiment is labeled as \"neutral.\" However, the review expresses both positive feelings about the colors and some dissatisfaction regarding the fit. A more accurate sentiment might be \"mixed\" or \"positive with reservations.\"\n   - **Rating**: 0.6\n\n3. **Summary**:\n   - The summary captures the essence of the review well, noting the appreciation for colors and the critique of the fit. It is concise and accurately reflects the review's main points.\n   - **Rating**: 1.0\n\n4. **Personalized Message**:\n   - The personalized message is appropriate and acknowledges the reviewer's feedback positively. It thanks the reviewer for their input and indicates that their suggestions will be considered, which is a good customer service approach.\n   - **Rating**: 1.0\n\n5. **Retail Insight**:\n   - The retail insight is relevant and actionable, suggesting the need for a range of sizes and fits to accommodate different body types. This aligns well with the reviewer's comments about fit and size.\n   - **Rating**: 1.0\n\nBased on the evaluations, here is the final output:\n\n```json\n{\n  \"categories\": 1.0,\n  \"sentiment\": 0.6,\n  \"summary\": 1.0,\n  \"personalized_message\": 1.0,\n  \"retail_insight\": 1.0\n}\n```"
}
{
  "judge_response": "To evaluate the zero-shot model output, I will assess each key based on its accuracy and completeness in relation to the original review. The scale for rating is as follows:\n\n- 0: Completely inaccurate or missing\n- 0.1 - 0.4: Somewhat inaccurate or incomplete\n- 0.5 - 0.7: Mostly accurate but with minor issues\n- 0.8 - 1: Fully accurate and complete\n\nNow, I will rate each part of the output:\n\n1. **Categories**:\n   - The categories provided (division, department, class, Mt Category) accurately reflect the context of the review. The division, department, and class are all correctly identified as \"General,\" \"Dresses,\" and \"Dresses,\" respectively. The \"Mt Category\" as \"Apparel\" is also appropriate given the context.\n   - **Rating**: 1.0\n\n2. **Sentiment**:\n   - The sentiment is correctly identified as \"positive,\" which aligns with the enthusiastic tone of the review. The reviewer expresses love for the dress and satisfaction with its design and versatility.\n   - **Rating**: 1.0\n\n3. **Summary**:\n   - The summary accurately captures the essence of the review, noting the reviewer's strong satisfaction, the beautiful design, and the versatility of the dress for different occasions. It effectively conveys the main points without omitting any critical information.\n   - **Rating**: 1.0\n\n4. **Personalized Message**:\n   - The personalized message is well-crafted and expresses gratitude for the feedback. It acknowledges the reviewer's love for the dress and its versatility, which aligns well with the review content. It feels warm and engaging, making it appropriate for customer interaction.\n   - **Rating**: 1.0\n\n5. **Retail Insight**:\n   - The retail insight is relevant and actionable, suggesting that the versatility of dresses should be highlighted in marketing materials. This aligns with the review's emphasis on the dress being suitable for multiple occasions, indicating a good understanding of customer preferences.\n   - **Rating**: 1.0\n\nOverall, the zero-shot model output is accurate and complete across all evaluated categories. Here is the final evaluation in dictionary format:\n\n```json\n{\n  \"categories\": 1.0,\n  \"sentiment\": 1.0,\n  \"summary\": 1.0,\n  \"personalized_message\": 1.0,\n  \"retail_insight\": 1.0\n}\n```"
}
{
  "judge_response": "To evaluate the Zero-Shot LLM output, I will assess each key based on its accuracy and completeness in relation to the original review. The scale for rating each key is as follows:\n\n- 0: Completely inaccurate or irrelevant\n- 0.25: Somewhat inaccurate or missing key elements\n- 0.5: Partially accurate but lacks detail or clarity\n- 0.75: Mostly accurate with minor issues\n- 1: Completely accurate and comprehensive\n\nNow, I will rate each part of the output:\n\n1. **Categories**:\n   - The categories provided (division, department, class, Mt Category) accurately reflect the information in the original review. The classification of \"General\" for division, \"Bottoms\" for department, and \"Pants\" for class is correct. The addition of \"Apparel\" as a broader category is also appropriate.\n   - **Rating**: 1.0\n\n2. **Sentiment**:\n   - The sentiment is correctly identified as \"positive,\" which aligns with the overall tone of the review. The reviewer expresses satisfaction with the fit, durability, and washability of the pants.\n   - **Rating**: 1.0\n\n3. **Summary**:\n   - The summary captures the main points of the review effectively, highlighting the customer's satisfaction with fit, durability, and the decision to make a repeat purchase. It is concise and accurately reflects the review's content.\n   - **Rating**: 1.0\n\n4. **Personalized Message**:\n   - The personalized message is warm and acknowledges the customer's feedback, which is appropriate. It reflects the positive sentiment of the review and encourages the customer to enjoy their new purchase. However, it could have included a mention of the specific color (chocolate brown) to make it even more personalized.\n   - **Rating**: 0.75\n\n5. **Retail Insight**:\n   - The retail insight is practical and relevant, suggesting that the company should highlight fit and durability in marketing materials, which aligns with the review's emphasis on these features. The recommendation to offer a wider range of colors is also a good suggestion based on the customer's repeat purchase behavior.\n   - **Rating**: 1.0\n\nBased on the evaluations, here is the final output:\n\n```json\n{\n  \"categories\": 1.0,\n  \"sentiment\": 1.0,\n  \"summary\": 1.0,\n  \"personalized_message\": 0.75,\n  \"retail_insight\": 1.0\n}\n```"
}
{
  "judge_response": "To evaluate the zero-shot model output, I will assess each key based on its accuracy and completeness in relation to the original review. The scale for rating each key is as follows:\n\n- 0: Completely inaccurate or irrelevant\n- 0.25: Somewhat inaccurate or missing key elements\n- 0.5: Partially accurate but lacks detail or clarity\n- 0.75: Mostly accurate with minor issues\n- 1: Completely accurate and comprehensive\n\nNow, I will rate each part of the output:\n\n1. **Categories**:\n   - The categories provided (division, department, class, Mt Category) are accurate and align with the original review context. The \"Mt Category\" as \"Apparel\" is a reasonable classification for dresses.\n   - **Rating**: 1.0\n\n2. **Sentiment**:\n   - The sentiment is correctly identified as \"positive,\" reflecting the overall tone of the review, which expresses satisfaction with the dress.\n   - **Rating**: 1.0\n\n3. **Summary**:\n   - The summary captures the main points of the review, including the dress's design, fit, and fabric quality. It effectively conveys the reviewer's positive experience.\n   - **Rating**: 1.0\n\n4. **Personalized Message**:\n   - The personalized message is warm and appreciative, which is appropriate given the positive nature of the review. It acknowledges the reviewer's feedback and expresses gratitude.\n   - **Rating**: 1.0\n\n5. **Retail Insight**:\n   - The retail insight suggests offering more size options and styles, which is relevant given the reviewer's mention of fit. However, it could have included a suggestion about the fabric care, as the reviewer noted the dress washes well. This would enhance the insight's relevance.\n   - **Rating**: 0.75\n\nBased on the evaluations, here is the final output:\n\n```json\n{\n  \"categories\": 1.0,\n  \"sentiment\": 1.0,\n  \"summary\": 1.0,\n  \"personalized_message\": 1.0,\n  \"retail_insight\": 0.75\n}\n```"
}
{
  "judge_response": "To evaluate the zero-shot model output, I will assess each key based on its accuracy and relevance to the original review. The scale for rating each part is as follows:\n\n- 0: Completely inaccurate or irrelevant\n- 0.25: Somewhat inaccurate or partially relevant\n- 0.5: Moderately accurate but lacks detail or clarity\n- 0.75: Mostly accurate with minor issues\n- 1: Completely accurate and highly relevant\n\nNow, I will rate each part of the output:\n\n1. **Categories**:\n   - The categories provided (division, department, class, Mt Category) accurately reflect the information in the original review. The division, department, and class are all \"Dresses,\" and \"Fashion\" is a suitable overarching category.\n   - **Rating**: 1.0\n\n2. **Sentiment**:\n   - The sentiment is correctly identified as \"positive,\" which aligns with the reviewer's appreciation of the dress's embroidery and color mix, despite mentioning the price.\n   - **Rating**: 1.0\n\n3. **Summary**:\n   - The summary captures the essence of the review well, highlighting the appreciation for the embroidery and color mix, the acknowledgment of the price, and the belief in the dress's timelessness. It is concise and accurately reflects the review's main points.\n   - **Rating**: 1.0\n\n4. **Personalized Message**:\n   - The personalized message is warm and engaging, thanking the reviewer and acknowledging their thoughts. It encourages the reviewer to enjoy styling the dress, which is appropriate and relevant. However, it could be slightly more tailored to the specific details mentioned in the review (e.g., mentioning the specific colors or the seasonal styling).\n   - **Rating**: 0.75\n\n5. **Retail Insight**:\n   - The retail insight suggests a loyalty program or discounts for repeat customers, which is a reasonable strategy for enhancing perceived value, especially for higher-priced items. However, it could be more directly tied to the specific context of the review, such as emphasizing the importance of quality and timelessness in marketing.\n   - **Rating**: 0.75\n\nNow, I will compile the evaluation into a dictionary format:\n\n```json\n{\n  \"categories\": 1.0,\n  \"sentiment\": 1.0,\n  \"summary\": 1.0,\n  \"personalized_message\": 0.75,\n  \"retail_insight\": 0.75\n}\n```"
}
{
  "judge_response": "To evaluate the zero-shot model output, I will assess each key based on its accuracy and relevance to the original review. The scale for rating is as follows:\n\n- 0: Completely inaccurate or irrelevant\n- 0.1 - 0.4: Somewhat inaccurate or lacking in relevance\n- 0.5: Neutral or average quality\n- 0.6 - 0.9: Mostly accurate and relevant, with minor issues\n- 1: Completely accurate and highly relevant\n\nNow, I will rate each part of the output:\n\n1. **Categories**:\n   - The categories provided (division, department, class, Mt Category) accurately reflect the information in the original prompt. The \"Mt Category\" as \"Apparel\" is a reasonable addition, as it encompasses the other categories.\n   - **Rating**: 1.0\n\n2. **Sentiment**:\n   - The sentiment is correctly identified as \"positive,\" which aligns with the customer's expression of enchantment regarding the dress.\n   - **Rating**: 1.0\n\n3. **Summary**:\n   - The summary accurately captures the essence of the review, mentioning the enchantment felt by the customer and the successful finding of the dress at a local retailer. It is concise and reflects the main points of the review.\n   - **Rating**: 1.0\n\n4. **Personalized Message**:\n   - The personalized message is warm and engaging, thanking the customer for their feedback and expressing excitement about their experience. It aligns well with the positive sentiment of the review.\n   - **Rating**: 1.0\n\n5. **Retail Insight**:\n   - The retail insight suggests enhancing the online shopping experience, which is relevant given the customer's initial enchantment upon seeing the dress online. It provides a constructive suggestion that could help improve customer satisfaction.\n   - **Rating**: 0.9 (slightly less than perfect as it could be more specific about what enhancements might be effective)\n\nOverall Evaluation:\n```json\n{\n  \"categories\": 1.0,\n  \"sentiment\": 1.0,\n  \"summary\": 1.0,\n  \"personalized_message\": 1.0,\n  \"retail_insight\": 0.9\n}\n```"
}
{
  "judge_response": "To evaluate the zero-shot model output, I will assess each key based on its accuracy and relevance to the original review. The scale for rating is as follows:\n\n- 0: Completely inaccurate or irrelevant\n- 0.25: Somewhat inaccurate or partially relevant\n- 0.5: Moderately accurate or relevant, but with significant issues\n- 0.75: Mostly accurate or relevant, with minor issues\n- 1: Completely accurate and relevant\n\nNow, I will rate each part of the output:\n\n1. **Categories**:\n   - The categories provided (division, department, class, Mt Category) are accurate and align with the original review. The classification of \"Apparel\" is appropriate given the context of the review.\n   - **Rating**: 1.0\n\n2. **Sentiment**:\n   - The sentiment is correctly identified as \"negative,\" which reflects the customer's disappointment and frustration with the product.\n   - **Rating**: 1.0\n\n3. **Summary**:\n   - The summary accurately captures the main points of the review, including the sizing issue and the shrinkage after washing, as well as the customer's disappointment. It is concise and covers the essential aspects of the review.\n   - **Rating**: 1.0\n\n4. **Personalized Message**:\n   - The personalized message is empathetic and acknowledges the customer's experience. It addresses the issues raised in the review and expresses a commitment to improvement, which is appropriate for customer service communication.\n   - **Rating**: 1.0\n\n5. **Retail Insight**:\n   - The retail insight provides practical suggestions for improvement, such as reviewing sizing charts and washing instructions, as well as considering fabric quality. These insights are relevant and actionable based on the customer's feedback.\n   - **Rating**: 1.0\n\nOverall, the zero-shot model output is accurate and relevant across all categories, sentiment, summary, personalized message, and retail insight. \n\nFinal Evaluation:\n```json\n{\n  \"categories\": 1.0,\n  \"sentiment\": 1.0,\n  \"summary\": 1.0,\n  \"personalized_message\": 1.0,\n  \"retail_insight\": 1.0\n}\n```"
}
{
  "judge_response": "To evaluate the zero-shot model output, I will assess each key based on its accuracy and completeness in relation to the original review. The scale for rating each key is as follows:\n\n- 0: Completely inaccurate or missing information.\n- 0.1 - 0.4: Some relevant information, but significant inaccuracies or omissions.\n- 0.5 - 0.7: Mostly accurate with minor inaccuracies or omissions.\n- 0.8 - 1: Fully accurate and complete representation of the original review.\n\nNow, I will evaluate each part of the output:\n\n1. **Categories**:\n   - The categories provided (division, department, class, and Mt Category) accurately reflect the information in the original prompt. The addition of \"Mt Category\" as \"Cardigans\" is appropriate given the context of the review.\n   - **Rating**: 1.0\n\n2. **Sentiment**:\n   - The sentiment is correctly identified as \"positive,\" which aligns with the enthusiastic tone of the review. The reviewer expresses satisfaction with the cardigan's versatility and style.\n   - **Rating**: 1.0\n\n3. **Summary**:\n   - The summary accurately captures the essence of the review, highlighting the reviewer's appreciation for the cardigan's versatility and its compatibility with various outfits. It succinctly conveys the main points without introducing inaccuracies.\n   - **Rating**: 1.0\n\n4. **Personalized Message**:\n   - The personalized message is appropriate and reflects a positive engagement with the reviewer. It acknowledges the feedback and expresses gratitude, which is suitable for customer interaction. However, it could be slightly more tailored to mention the specific items (like yoga pants and jeans) for a more personalized touch.\n   - **Rating**: 0.9\n\n5. **Retail Insight**:\n   - The retail insight suggests expanding the range of colors and styles for cardigans, which is a reasonable recommendation based on the review's emphasis on versatility. However, it could be more specific about the types of styles or colors that might appeal to customers, as the review does not explicitly mention a desire for more options.\n   - **Rating**: 0.8\n\nNow, compiling the evaluations into a dictionary format:\n\n```json\n{\n  \"categories\": 1.0,\n  \"sentiment\": 1.0,\n  \"summary\": 1.0,\n  \"personalized_message\": 0.9,\n  \"retail_insight\": 0.8\n}\n```"
}
{
  "judge_response": "To evaluate the zero-shot model output, I will assess each key based on its accuracy and relevance to the original review. The scale for rating each part is as follows:\n\n- 0: Completely inaccurate or irrelevant\n- 0.25: Somewhat inaccurate or partially relevant\n- 0.5: Moderately accurate but lacking detail or clarity\n- 0.75: Mostly accurate with minor issues\n- 1: Completely accurate and highly relevant\n\nNow, I will rate each part of the output:\n\n1. **Categories**:\n   - The categories provided (division, department, class, and Mt Category) accurately reflect the information in the original review. The division \"General Petite,\" department \"Tops,\" and class \"Fine gauge\" are all correct. The addition of \"Mt Category\" as \"Apparel\" is also appropriate, as it encompasses the product type.\n   - **Rating**: 1\n\n2. **Sentiment**:\n   - The sentiment is correctly identified as \"positive.\" The review expresses enthusiasm and satisfaction with the product, which aligns with the sentiment classification.\n   - **Rating**: 1\n\n3. **Summary**:\n   - The summary captures the essence of the review well, noting the reviewer's enthusiasm for the colors, design features, and fit. It also mentions the styling choice, which is a key aspect of the review. However, it could be slightly more concise.\n   - **Rating**: 0.75\n\n4. **Personalized Message**:\n   - The personalized message is warm and engaging, thanking the reviewer and acknowledging their positive feedback. It also encourages sharing styling tips, which is relevant and adds a personal touch. However, it could be more tailored to the specific details mentioned in the review.\n   - **Rating**: 0.75\n\n5. **Retail Insight**:\n   - The retail insight is practical and relevant, suggesting that showcasing customer styling ideas could enhance engagement. This aligns well with the review's emphasis on styling, making it a valuable suggestion for retailers.\n   - **Rating**: 1\n\nNow, I will compile the ratings into a dictionary format:\n\n```json\n{\n  \"categories\": 1,\n  \"sentiment\": 1,\n  \"summary\": 0.75,\n  \"personalized_message\": 0.75,\n  \"retail_insight\": 1\n}\n```"
}
{
  "judge_response": "To evaluate the zero-shot model output, I will assess each key based on its accuracy and relevance to the original review. The scale for rating each key is as follows:\n\n- 0: Completely inaccurate or irrelevant\n- 0.25: Somewhat inaccurate or partially relevant\n- 0.5: Neutral; neither accurate nor inaccurate\n- 0.75: Mostly accurate but with minor issues\n- 1: Completely accurate and relevant\n\nNow, I will evaluate each part of the output:\n\n1. **Categories**:\n   - The categories provided (division, department, class, and Mt Category) are mostly accurate. The division and department are correctly identified as \"Intimates\" and \"Intimate,\" respectively. The class is labeled as \"Lounge,\" which aligns with the context of the review. However, the \"Mt Category\" as \"Cover-ups\" is a bit misleading since the review emphasizes its versatility beyond just being a cover-up. \n   - **Rating**: 0.75\n\n2. **Sentiment**:\n   - The sentiment is correctly identified as \"positive,\" reflecting the reviewer's satisfaction with the tunic's versatility and functionality. \n   - **Rating**: 1\n\n3. **Summary**:\n   - The summary accurately captures the essence of the review, highlighting the tunic's versatility and its transition from a swim cover-up to a layering piece over skinny jeans. It succinctly conveys the main points of the review.\n   - **Rating**: 1\n\n4. **Personalized Message**:\n   - The personalized message is warm and engaging, expressing gratitude for the feedback and reinforcing the positive experience shared by the reviewer. It aligns well with the sentiment of the review and encourages further engagement.\n   - **Rating**: 1\n\n5. **Retail Insight**:\n   - The retail insight is relevant and actionable, suggesting that highlighting product versatility in marketing can resonate with customers. This aligns with the review's emphasis on the tunic's multiple uses.\n   - **Rating**: 1\n\nBased on the evaluations, here is the final output:\n\n```json\n{\n  \"categories\": 0.75,\n  \"sentiment\": 1,\n  \"summary\": 1,\n  \"personalized_message\": 1,\n  \"retail_insight\": 1\n}\n```"
}
{
  "judge_response": "To evaluate the zero-shot model output, I will assess each key based on its accuracy and completeness in relation to the original review. The scale for rating will be as follows:\n\n- 0: Completely inaccurate or irrelevant\n- 0.25: Somewhat inaccurate or missing key elements\n- 0.5: Partially accurate but lacks detail or clarity\n- 0.75: Mostly accurate with minor issues\n- 1: Completely accurate and comprehensive\n\nNow, I will rate each part of the output:\n\n1. **Categories**:\n   - The categories provided (division, department, class, Mt Category) are accurate and align with the review context. The classification of \"General\" for division, \"Tops\" for department, and \"Knits\" for class is correct. The addition of \"Apparel\" as a category is also appropriate.\n   - **Rating**: 1.0\n\n2. **Sentiment**:\n   - The sentiment is correctly identified as \"positive,\" reflecting the reviewer's overall enjoyment of the top despite mentioning a minor issue with the length.\n   - **Rating**: 1.0\n\n3. **Summary**:\n   - The summary captures the essence of the review well, noting the reviewer's appreciation for the top's cuteness and lightweight lace while acknowledging the concern about its length. However, it could have included the versatility aspect mentioned in the review regarding styling options.\n   - **Rating**: 0.75\n\n4. **Personalized Message**:\n   - The personalized message is warm and acknowledges the reviewer's thoughts, which is appropriate for customer engagement. It reflects the positive sentiment and encourages further styling, aligning well with the review's content.\n   - **Rating**: 1.0\n\n5. **Retail Insight**:\n   - The retail insight is practical and relevant, suggesting the offering of a range of lengths and styling suggestions, which directly addresses the reviewer's concern about fit and versatility. This insight is actionable and beneficial for enhancing customer experience.\n   - **Rating**: 1.0\n\nBased on the evaluations, here is the final output:\n\n```json\n{\n  \"categories\": 1.0,\n  \"sentiment\": 1.0,\n  \"summary\": 0.75,\n  \"personalized_message\": 1.0,\n  \"retail_insight\": 1.0\n}\n```"
}
{
  "judge_response": "To evaluate the zero-shot model output, I will assess each key based on its accuracy and completeness in relation to the original review. The scale for rating will be as follows:\n\n- 0: Completely inaccurate or irrelevant\n- 0.25: Somewhat inaccurate or missing key elements\n- 0.5: Partially accurate but lacks detail or clarity\n- 0.75: Mostly accurate with minor issues\n- 1: Completely accurate and comprehensive\n\nNow, I will rate each part of the output:\n\n1. **Categories**:\n   - The categories provided (division, department, class, Mt Category) are accurate and align with the original review. The addition of \"Mt Category\" as \"Outerwear\" is appropriate given that jackets fall under this category.\n   - **Rating**: 1.0\n\n2. **Sentiment**:\n   - The sentiment is correctly identified as \"positive,\" reflecting the reviewer's overall satisfaction with the jacket.\n   - **Rating**: 1.0\n\n3. **Summary**:\n   - The summary captures the main points of the review, including the reviewer's appreciation for the color and cut, the suitability for fall layering, and the note about the sleeves being shorter than expected. It is concise and accurately reflects the review.\n   - **Rating**: 1.0\n\n4. **Personalized Message**:\n   - The personalized message is warm and acknowledges the reviewer's feedback. It highlights the positive aspects mentioned in the review and addresses the concern about sleeve length, making it relevant and engaging.\n   - **Rating**: 1.0\n\n5. **Retail Insight**:\n   - The retail insight provides a constructive suggestion to improve customer expectations regarding sleeve design and fit. This is a valuable recommendation based on the reviewer's experience and is relevant to potential improvements in product presentation.\n   - **Rating**: 1.0\n\nOverall, the zero-shot model output is accurate and comprehensive across all evaluated categories. Here is the final evaluation in dictionary format:\n\n```json\n{\n  \"categories\": 1.0,\n  \"sentiment\": 1.0,\n  \"summary\": 1.0,\n  \"personalized_message\": 1.0,\n  \"retail_insight\": 1.0\n}\n```"
}
{
  "judge_response": "To evaluate the zero-shot model output, I will assess each key based on its accuracy and completeness in relation to the original review. The scale for rating each key is as follows:\n\n- 0: Completely inaccurate or irrelevant\n- 0.25: Somewhat inaccurate or missing key elements\n- 0.5: Partially accurate but lacks detail or clarity\n- 0.75: Mostly accurate with minor issues\n- 1: Completely accurate and comprehensive\n\nNow, I will rate each part of the output:\n\n1. **Categories**:\n   - The categories provided (division, department, class, Mt Category) accurately reflect the information in the original review. The division is \"General,\" the department is \"Tops,\" and the class is \"Knits,\" which are all correct. The \"Mt Category\" as \"Apparel\" is also appropriate.\n   - **Rating**: 1.0\n\n2. **Sentiment**:\n   - The sentiment is labeled as \"positive,\" which aligns well with the reviewer's overall tone and feelings expressed in the review. The reviewer expresses enjoyment of the tee and its design, indicating a positive sentiment.\n   - **Rating**: 1.0\n\n3. **Summary**:\n   - The summary captures the main points of the review accurately, noting the cut, fit, and the reviewer's hope for quality. It mentions the tee being cute and the strap detail, which are key aspects of the review.\n   - **Rating**: 1.0\n\n4. **Personalized Message**:\n   - The personalized message is warm and acknowledges the reviewer's thoughts, which is appropriate. It expresses appreciation for the review and aligns with the positive sentiment of the review. However, it could have included a reference to the sizing issue mentioned by the reviewer.\n   - **Rating**: 0.75\n\n5. **Retail Insight**:\n   - The retail insight is practical and relevant, suggesting the provision of detailed sizing information, which is a direct response to the reviewer's comment about the tee running smaller. This insight is valuable for improving customer experience.\n   - **Rating**: 1.0\n\nBased on the evaluations, here is the final output:\n\n```json\n{\n  \"categories\": 1.0,\n  \"sentiment\": 1.0,\n  \"summary\": 1.0,\n  \"personalized_message\": 0.75,\n  \"retail_insight\": 1.0\n}\n```"
}
{
  "judge_response": "To evaluate the zero-shot model output, I will assess each key based on its accuracy and completeness in relation to the original review. The scale for rating is as follows:\n\n- 0: Completely inaccurate or irrelevant\n- 0.25: Somewhat inaccurate or missing key elements\n- 0.5: Partially accurate but lacks depth or clarity\n- 0.75: Mostly accurate with minor issues\n- 1: Completely accurate and comprehensive\n\nNow, I will evaluate each part of the output:\n\n1. **Categories**:\n   - The categories provided (division, department, class, Mt Category) accurately reflect the information in the original prompt. The \"Mt Category\" of \"Apparel\" is a reasonable classification for the \"Tops\" department.\n   - **Rating**: 1.0\n\n2. **Sentiment**:\n   - The sentiment is labeled as \"negative,\" which aligns with the reviewer's overall impression of the product being overpriced despite some positive comments. The sentiment captures the essence of the review accurately.\n   - **Rating**: 1.0\n\n3. **Summary**:\n   - The summary effectively encapsulates the main points of the review: the reviewer finds the top cute, appreciates the colors and soft material, but feels it is overpriced due to the thin fabric. It is clear and concise.\n   - **Rating**: 1.0\n\n4. **Personalized Message**:\n   - The personalized message acknowledges the positive aspects mentioned by the reviewer and expresses appreciation for their feedback. It also addresses the concern about pricing, which is relevant. However, it could be slightly more empathetic by acknowledging the disappointment regarding the fabric quality.\n   - **Rating**: 0.75\n\n5. **Retail Insight**:\n   - The retail insight provides actionable suggestions based on the review, such as evaluating pricing strategy and enhancing fabric quality or product descriptions. This is relevant and useful for improving customer satisfaction. However, it could be more specific about how to implement these suggestions.\n   - **Rating**: 0.75\n\nNow, I will compile the evaluation into a dictionary format:\n\n```json\n{\n  \"categories\": 1.0,\n  \"sentiment\": 1.0,\n  \"summary\": 1.0,\n  \"personalized_message\": 0.75,\n  \"retail_insight\": 0.75\n}\n```"
}
{
  "judge_response": "To evaluate the zero-shot model output, I will assess each key based on its accuracy and completeness in relation to the original review. The scale for rating will be as follows:\n\n- 0: Completely inaccurate or irrelevant\n- 0.25: Somewhat inaccurate or lacking detail\n- 0.5: Partially accurate but missing key elements\n- 0.75: Mostly accurate with minor issues\n- 1: Completely accurate and comprehensive\n\nNow, I will rate each part of the output:\n\n1. **Categories**:\n   - The categories provided (division, department, class, Mt Category) are accurate and align with the original review context. The \"Mt Category\" as \"Apparel\" is also appropriate.\n   - **Rating**: 1.0\n\n2. **Sentiment**:\n   - The sentiment is correctly identified as \"positive,\" reflecting the overall tone of the review, which highlights compliments and appreciation for the dress despite some minor issues.\n   - **Rating**: 1.0\n\n3. **Summary**:\n   - The summary captures the essence of the review well, mentioning the beauty of the dress, the compliments received, and the issues with fit and straps. However, it could have included the specific size issue (the reviewer typically wears 10-12 but sized up to 14) for completeness.\n   - **Rating**: 0.75\n\n4. **Personalized Message**:\n   - The personalized message is warm and acknowledges the reviewer's feedback effectively. It expresses gratitude and addresses the positive aspects while also recognizing the issues mentioned. However, it could have been slightly more tailored by mentioning the specific issues with the zipper and straps.\n   - **Rating**: 0.75\n\n5. **Retail Insight**:\n   - The retail insight is practical and relevant, suggesting improvements like a size guide and addressing fit issues in product descriptions. This aligns well with the review's content and provides actionable advice for the retailer.\n   - **Rating**: 1.0\n\nNow, compiling the ratings into a dictionary format:\n\n```json\n{\n  \"categories\": 1.0,\n  \"sentiment\": 1.0,\n  \"summary\": 0.75,\n  \"personalized_message\": 0.75,\n  \"retail_insight\": 1.0\n}\n```"
}
{
  "judge_response": "To evaluate the zero-shot model output, I will assess each key based on its accuracy and completeness in relation to the original review provided. The scale for rating each key is as follows:\n\n- 0: Completely inaccurate or missing\n- 0.1 - 0.9: Partially accurate, with varying degrees of completeness or relevance\n- 1: Completely accurate and fully captures the essence of the original review\n\nNow, I will evaluate each part of the output:\n\n1. **Categories**:\n   - The categories provided (division, department, class, Mt Category) accurately reflect the information in the original review. The division \"General Petite,\" department \"Jackets,\" and class \"Jackets\" are all correct. The addition of \"Mt Category\" as \"Apparel\" is also appropriate, as jackets fall under this broader category.\n   - **Rating**: 1.0\n\n2. **Sentiment**:\n   - The sentiment is labeled as \"positive,\" which aligns with the reviewer's expression of finding the jacket \"adorable\" and appreciating the \"great\" pink color. There are no negative sentiments expressed, making this assessment accurate.\n   - **Rating**: 1.0\n\n3. **Summary**:\n   - The summary captures the main points of the review: the customer's positive feelings about the jacket, the issue with sleeve length, and the intention to try a petite size. It effectively summarizes the key aspects without omitting important details.\n   - **Rating**: 1.0\n\n4. **Personalized Message**:\n   - The personalized message is warm and acknowledges the customer's feedback positively. It reflects the customer's sentiments about the color and style while expressing hope that the petite size will fit better. This message is appropriate and engaging.\n   - **Rating**: 1.0\n\n5. **Retail Insight**:\n   - The retail insight suggests offering more size options or adjustable sleeve lengths, which is a relevant recommendation based on the reviewer's feedback about sleeve length. This insight is actionable and directly addresses a potential area for improvement in customer satisfaction.\n   - **Rating**: 1.0\n\nBased on the evaluations, here is the final output:\n\n```json\n{\n  \"categories\": 1.0,\n  \"sentiment\": 1.0,\n  \"summary\": 1.0,\n  \"personalized_message\": 1.0,\n  \"retail_insight\": 1.0\n}\n```"
}
{
  "judge_response": "To evaluate the Zero-Shot LLM output, I will assess each key based on its accuracy and relevance to the original review. The scale for rating is as follows:\n\n- 0: Completely inaccurate or irrelevant\n- 0.25: Somewhat inaccurate or partially relevant\n- 0.5: Moderately accurate but lacking in detail or clarity\n- 0.75: Mostly accurate with minor issues\n- 1: Completely accurate and relevant\n\nNow, I will rate each part of the output:\n\n1. **Categories**:\n   - The categories provided (division, department, class, Mt Category) are accurate and align with the original review. The classification of \"General\" for division, \"Tops\" for department, and \"Knits\" for class is correct. The addition of \"Apparel\" as a category is also appropriate.\n   - **Rating**: 1.0\n\n2. **Sentiment**:\n   - The sentiment is correctly identified as \"negative,\" reflecting the customer's dissatisfaction with the fit of the knit top. The review clearly expresses disappointment, which the sentiment captures accurately.\n   - **Rating**: 1.0\n\n3. **Summary**:\n   - The summary accurately encapsulates the main points of the review: the customer found the top boxy and unflattering, which led to a return. It is concise and directly reflects the customer's experience.\n   - **Rating**: 1.0\n\n4. **Personalized Message**:\n   - The personalized message is appropriate and empathetic, acknowledging the customer's feedback and expressing regret that the product did not meet expectations. It also indicates that the feedback will be considered for future designs, which is a positive touch.\n   - **Rating**: 1.0\n\n5. **Retail Insight**:\n   - The retail insight provides a constructive suggestion to improve customer experience by offering more detailed sizing information and fit descriptions. This is relevant to the review and addresses the issue of returns effectively.\n   - **Rating**: 1.0\n\nBased on the evaluations, here is the final output:\n\n```json\n{\n  \"categories\": 1.0,\n  \"sentiment\": 1.0,\n  \"summary\": 1.0,\n  \"personalized_message\": 1.0,\n  \"retail_insight\": 1.0\n}\n```"
}
{
  "judge_response": "To evaluate the zero-shot model output, I will assess each key based on its accuracy and completeness in relation to the original review. The scale for rating each key is as follows:\n\n- 0: Completely inaccurate or irrelevant\n- 0.25: Somewhat inaccurate or missing key elements\n- 0.5: Partially accurate but lacks detail or clarity\n- 0.75: Mostly accurate with minor issues\n- 1: Completely accurate and comprehensive\n\nNow, I will rate each part of the output:\n\n1. **Categories**:\n   - The categories provided (division, department, class, Mt Category) are accurate and align with the original review context. The \"Mt Category\" as \"Apparel\" is also appropriate.\n   - **Rating**: 1.0\n\n2. **Sentiment**:\n   - The sentiment is correctly identified as \"positive,\" reflecting the overall tone of the review, which expresses appreciation for the dress's color and fabric.\n   - **Rating**: 1.0\n\n3. **Summary**:\n   - The summary captures the main points of the review, highlighting the positive aspects (color and fabric) and the critique regarding the length. It is concise and accurately reflects the review's content.\n   - **Rating**: 1.0\n\n4. **Personalized Message**:\n   - The personalized message is appropriate and acknowledges the reviewer's feedback positively. It thanks the reviewer and addresses the suggestion about the length, which is relevant to the review.\n   - **Rating**: 1.0\n\n5. **Retail Insight**:\n   - The retail insight is practical and directly addresses the reviewer's concern about the dress length. It suggests a potential solution (offering a longer version or length options), which is a valuable insight for retail strategy.\n   - **Rating**: 1.0\n\nBased on the evaluations, here is the final output:\n\n```json\n{\n  \"categories\": 1.0,\n  \"sentiment\": 1.0,\n  \"summary\": 1.0,\n  \"personalized_message\": 1.0,\n  \"retail_insight\": 1.0\n}\n```"
}
{
  "judge_response": "To evaluate the zero-shot model output, I will assess each key based on its accuracy and completeness in relation to the original review. The scale for rating each key is as follows:\n\n- 0: Completely inaccurate or irrelevant\n- 0.25: Somewhat inaccurate or missing key elements\n- 0.5: Partially accurate but lacks detail or clarity\n- 0.75: Mostly accurate with minor issues\n- 1: Completely accurate and comprehensive\n\nNow, I will evaluate each part of the output:\n\n1. **Categories**:\n   - The categories provided (division, department, class, Mt Category) accurately reflect the information in the original review. The division \"General Petite,\" department \"Tops,\" and class \"Blouses\" are all correct. The addition of \"Mt Category\" as \"Apparel\" is also appropriate as it encompasses the other categories.\n   - **Rating**: 1.0\n\n2. **Sentiment**:\n   - The sentiment is correctly identified as \"positive.\" The review expresses appreciation for the color and indicates a willingness to order more, which aligns with a positive sentiment.\n   - **Rating**: 1.0\n\n3. **Summary**:\n   - The summary accurately captures the main points of the review: the appreciation for the color, the note about the odd fit, and the conclusion that the positives outweigh the negatives. It is concise and reflects the review's content well.\n   - **Rating**: 1.0\n\n4. **Personalized Message**:\n   - The personalized message is friendly and acknowledges the customer's feedback about the color and fit. It encourages further exploration of other colors, which aligns well with the customer's intent to order more. However, it could have included a more direct acknowledgment of the fit issue.\n   - **Rating**: 0.75\n\n5. **Retail Insight**:\n   - The retail insight is practical and relevant, suggesting that providing detailed sizing information and fit guides could help address the fit-related concerns mentioned in the review. This insight is actionable and aligns with the customer's feedback.\n   - **Rating**: 1.0\n\nBased on the evaluations, here is the final output:\n\n```json\n{\n  \"categories\": 1.0,\n  \"sentiment\": 1.0,\n  \"summary\": 1.0,\n  \"personalized_message\": 0.75,\n  \"retail_insight\": 1.0\n}\n```"
}
{
  "judge_response": "To evaluate the zero-shot model output, I will assess each key based on its accuracy and completeness in relation to the original review. The scale for rating is as follows:\n\n- 0: Completely inaccurate or irrelevant\n- 0.25: Somewhat inaccurate or lacking detail\n- 0.5: Partially accurate but missing key elements\n- 0.75: Mostly accurate with minor issues\n- 1: Completely accurate and comprehensive\n\nNow, I will rate each part of the output:\n\n1. **Categories**:\n   - The categories provided (division, department, class, Mt Category) are accurate and align with the original review. The classification of \"Apparel\" is appropriate for the context.\n   - **Rating**: 1.0\n\n2. **Sentiment**:\n   - The sentiment is correctly identified as \"negative,\" which reflects the reviewer's disappointment and frustration with the product.\n   - **Rating**: 1.0\n\n3. **Summary**:\n   - The summary accurately captures the main points of the review, including the disappointment with quality, fit, and material, while acknowledging the positive aspect of the design. It is concise and relevant.\n   - **Rating**: 1.0\n\n4. **Personalized Message**:\n   - The personalized message is appropriate and empathetic, acknowledging the customer's feedback and expressing a commitment to improvement. However, it could be slightly more tailored to the specific issues raised in the review (e.g., mentioning the material quality).\n   - **Rating**: 0.75\n\n5. **Retail Insight**:\n   - The retail insight provides actionable recommendations based on the review, such as improving material quality and fit, which are directly relevant to the customer's complaints. It also suggests enhancing product descriptions, which is a valuable addition.\n   - **Rating**: 1.0\n\nBased on the evaluations, here is the final output:\n\n```json\n{\n  \"categories\": 1.0,\n  \"sentiment\": 1.0,\n  \"summary\": 1.0,\n  \"personalized_message\": 0.75,\n  \"retail_insight\": 1.0\n}\n```"
}
{
  "judge_response": "To evaluate the zero-shot model output, I will assess each key based on its accuracy and completeness in relation to the original review. The scale for rating is as follows:\n\n- 0: Completely inaccurate or irrelevant\n- 0.25: Somewhat inaccurate or lacking detail\n- 0.5: Partially accurate but missing key elements\n- 0.75: Mostly accurate with minor issues\n- 1: Completely accurate and comprehensive\n\nNow, I will rate each part of the output:\n\n1. **Categories**:\n   - The categories provided (division, department, class, Mt Category) are accurate and align with the original review context. The classification of \"Apparel\" is appropriate for the dress category.\n   - **Rating**: 1.0\n\n2. **Sentiment**:\n   - The sentiment is correctly identified as \"positive,\" reflecting the customer's overall satisfaction with the dress after trying it on, despite initial skepticism.\n   - **Rating**: 1.0\n\n3. **Summary**:\n   - The summary accurately captures the essence of the review, highlighting the customer's initial doubts and subsequent pleasant surprise regarding the fit and style of the dress. It succinctly conveys the main points without omitting important details.\n   - **Rating**: 1.0\n\n4. **Personalized Message**:\n   - The personalized message is warm and acknowledges the customer's experience, expressing gratitude and excitement about their satisfaction. It is relevant and appropriate for the context of the review.\n   - **Rating**: 1.0\n\n5. **Retail Insight**:\n   - The retail insight provides a valuable suggestion to enhance product descriptions and images, which is relevant given the customer's initial perception of the dress. It addresses a potential area for improvement based on the review.\n   - **Rating**: 1.0\n\nBased on the evaluations, here is the final output:\n\n```json\n{\n  \"categories\": 1.0,\n  \"sentiment\": 1.0,\n  \"summary\": 1.0,\n  \"personalized_message\": 1.0,\n  \"retail_insight\": 1.0\n}\n```"
}
{
  "judge_response": "To evaluate the Zero-Shot LLM output, I will assess each key based on its accuracy and relevance to the original review. The scale for rating each part is as follows:\n\n- 0: Completely inaccurate or irrelevant\n- 0.25: Somewhat inaccurate or partially relevant\n- 0.5: Moderately accurate but lacks detail or clarity\n- 0.75: Mostly accurate with minor issues\n- 1: Completely accurate and relevant\n\nNow, I will rate each part of the output:\n\n1. **Categories**:\n   - The categories provided (division, department, class, Mt Category) accurately reflect the information in the original review. The \"Mt Category\" as \"Apparel\" is a reasonable classification for the product type.\n   - **Rating**: 1.0\n\n2. **Sentiment**:\n   - The sentiment is correctly identified as \"positive.\" The review expresses satisfaction with the product's shape and quality, despite mentioning a minor issue with snugness.\n   - **Rating**: 1.0\n\n3. **Summary**:\n   - The summary captures the main points of the review well, highlighting the flattering shape and quality while acknowledging the snug lace detail. It is concise and accurately reflects the review's content.\n   - **Rating**: 1.0\n\n4. **Personalized Message**:\n   - The personalized message is appropriate and acknowledges the reviewer's feedback positively. It addresses the flattering shape and quality while also recognizing the snug lace detail, which shows attentiveness to the reviewer's experience.\n   - **Rating**: 1.0\n\n5. **Retail Insight**:\n   - The retail insight suggests offering a wider range of sizes or adjustable features, which is a constructive recommendation based on the reviewer's comment about snugness. This insight is relevant and actionable.\n   - **Rating**: 1.0\n\nBased on the evaluations, here is the final output:\n\n```json\n{\n  \"categories\": 1.0,\n  \"sentiment\": 1.0,\n  \"summary\": 1.0,\n  \"personalized_message\": 1.0,\n  \"retail_insight\": 1.0\n}\n```"
}
{
  "judge_response": "To evaluate the zero-shot model output, I will assess each key based on its accuracy and relevance to the original review. The scale for rating each key is as follows:\n\n- 0: Completely inaccurate or irrelevant\n- 0.25: Somewhat inaccurate or partially relevant\n- 0.5: Neutral; neither accurate nor inaccurate\n- 0.75: Mostly accurate with minor issues\n- 1: Completely accurate and relevant\n\nNow, I will rate each part of the output:\n\n1. **Categories**:\n   - The categories provided (division, department, class, Mt Category) accurately reflect the information in the review. The division and department are correctly identified as \"Intimates\" and \"Intimate,\" respectively, and the class is correctly identified as \"Legwear.\" The addition of \"Mt Category\" as \"Leggings\" is also appropriate given the context of the review.\n   - **Rating**: 1.0\n\n2. **Sentiment**:\n   - The sentiment is labeled as \"neutral.\" However, the review expresses both positive and negative feelings, indicating a mixed sentiment rather than neutral. The reviewer appreciates the softness and design but has significant concerns about sizing and functionality, which suggests a more nuanced sentiment.\n   - **Rating**: 0.5\n\n3. **Summary**:\n   - The summary captures the main points of the review, highlighting the softness and design of the leggings while also addressing the sizing issue. It accurately reflects the reviewer's experience and concerns.\n   - **Rating**: 1.0\n\n4. **Personalized Message**:\n   - The personalized message is well-crafted, acknowledging the reviewer's feedback and expressing appreciation for their detailed comments. It addresses the positive aspects while also recognizing the concerns about sizing, which is appropriate and empathetic.\n   - **Rating**: 1.0\n\n5. **Retail Insight**:\n   - The retail insight provides practical suggestions based on the review. It correctly identifies the need for a size guide and recommendations for sizing down, as well as the importance of maintaining shape after wear. This insight is relevant and actionable.\n   - **Rating**: 1.0\n\nBased on the evaluations, here is the final output:\n\n```json\n{\n  \"categories\": 1.0,\n  \"sentiment\": 0.5,\n  \"summary\": 1.0,\n  \"personalized_message\": 1.0,\n  \"retail_insight\": 1.0\n}\n```"
}
{
  "judge_response": "To evaluate the zero-shot model output, I will assess each key based on its accuracy and completeness in relation to the original review. The scale for rating will be as follows:\n\n- 0: Completely inaccurate or irrelevant\n- 0.25: Somewhat inaccurate or lacking detail\n- 0.5: Partially accurate but missing key elements\n- 0.75: Mostly accurate with minor issues\n- 1: Completely accurate and comprehensive\n\nNow, I will rate each part of the output:\n\n1. **Categories**:\n   - The categories provided (division, department, class, Mt Category) are accurate and align with the original review's context. The addition of \"Mt Category\" as \"Apparel\" is appropriate as it encompasses the dress category.\n   - **Rating**: 1.0\n\n2. **Sentiment**:\n   - The sentiment is labeled as \"neutral,\" which is appropriate given the reviewer's mixed feelings about the fit of the dress. The reviewer expresses both satisfaction with the size small and dissatisfaction with the size extra small, which justifies a neutral sentiment.\n   - **Rating**: 1.0\n\n3. **Summary**:\n   - The summary accurately captures the main points of the review, including the fit of the sizes and the mention of the detachable slip. It reflects the reviewer's experience well and provides a concise overview.\n   - **Rating**: 1.0\n\n4. **Personalized Message**:\n   - The personalized message is polite and acknowledges the reviewer's feedback, which is a good practice in customer service. However, it could be slightly more tailored to the specific issues raised in the review (e.g., mentioning the fit issues directly).\n   - **Rating**: 0.75\n\n5. **Retail Insight**:\n   - The retail insight is practical and relevant, suggesting improvements that could enhance the shopping experience. It addresses the need for detailed sizing charts and fit guides, which is a direct response to the review's content.\n   - **Rating**: 1.0\n\nBased on the evaluations, here is the final output:\n\n```json\n{\n  \"categories\": 1.0,\n  \"sentiment\": 1.0,\n  \"summary\": 1.0,\n  \"personalized_message\": 0.75,\n  \"retail_insight\": 1.0\n}\n```"
}
{
  "judge_response": "To evaluate the zero-shot model output, I will assess each key based on its accuracy and relevance to the original review. The scale for rating is as follows:\n\n- 0: Completely inaccurate or irrelevant\n- 0.25: Somewhat inaccurate or partially relevant\n- 0.5: Moderately accurate but lacking detail or clarity\n- 0.75: Mostly accurate with minor issues\n- 1: Completely accurate and highly relevant\n\nNow, I will rate each part of the output:\n\n1. **Categories**:\n   - The categories provided (division, department, class, Mt Category) are accurate and align with the original review. The classification of \"Apparel\" is appropriate given the context of the review.\n   - **Rating**: 1.0\n\n2. **Sentiment**:\n   - The sentiment is correctly identified as \"negative,\" which reflects the customer's disappointment with the product due to discomfort and design issues.\n   - **Rating**: 1.0\n\n3. **Summary**:\n   - The summary accurately captures the main points of the review, highlighting the discomfort of the neck and the issue with the liner's length. It succinctly conveys the customer's disappointment.\n   - **Rating**: 1.0\n\n4. **Personalized Message**:\n   - The personalized message is appropriate and empathetic, acknowledging the customer's feedback and expressing regret for their experience. It effectively communicates that the customer's insights are valued.\n   - **Rating**: 1.0\n\n5. **Retail Insight**:\n   - The retail insight provides a constructive suggestion to improve the product design, specifically addressing the neck area and liner length. This is relevant and actionable based on the customer's feedback.\n   - **Rating**: 1.0\n\nOverall, the zero-shot model output is accurate and complete in all aspects based on the original review. \n\nFinal Evaluation:\n```json\n{\n  \"categories\": 1.0,\n  \"sentiment\": 1.0,\n  \"summary\": 1.0,\n  \"personalized_message\": 1.0,\n  \"retail_insight\": 1.0\n}\n```"
}
{
  "judge_response": "To evaluate the zero-shot model output, I will assess each key based on its accuracy and completeness in relation to the original review provided in the prompt. The scale for rating will be as follows:\n\n- 0: Completely inaccurate or irrelevant\n- 0.25: Somewhat inaccurate or missing key elements\n- 0.5: Partially accurate but lacks detail or clarity\n- 0.75: Mostly accurate with minor issues\n- 1: Completely accurate and comprehensive\n\nNow, I will rate each part of the output:\n\n1. **Categories**:\n   - The categories provided (division, department, class, Mt Category) accurately reflect the information in the original review. The division is \"General,\" the department is \"Tops,\" and the class is \"Blouses,\" which are all correct. The addition of \"Mt Category\" as \"Apparel\" is also appropriate as it encompasses the other categories.\n   - **Rating**: 1.0\n\n2. **Sentiment**:\n   - The sentiment is labeled as \"positive,\" which aligns well with the review's enthusiastic tone and expressions of satisfaction (e.g., \"love the contrast,\" \"just what I had hoped it would be\").\n   - **Rating**: 1.0\n\n3. **Summary**:\n   - The summary captures the essence of the review, highlighting the appreciation for intricate detail and color contrast, as well as the suitability for holiday parties. It is concise and accurately reflects the reviewer's sentiments.\n   - **Rating**: 1.0\n\n4. **Personalized Message**:\n   - The personalized message is warm and engaging, thanking the reviewer and expressing happiness that the product met their expectations. It effectively connects with the review's positive tone and context.\n   - **Rating**: 1.0\n\n5. **Retail Insight**:\n   - The retail insight suggests highlighting intricate details and color contrasts in product descriptions, which is a relevant and actionable recommendation based on the review. It aligns well with the review's focus on these aspects.\n   - **Rating**: 1.0\n\nOverall, the zero-shot model output is accurate and comprehensive across all evaluated categories. \n\nFinal Evaluation:\n```json\n{\n  \"categories\": 1.0,\n  \"sentiment\": 1.0,\n  \"summary\": 1.0,\n  \"personalized_message\": 1.0,\n  \"retail_insight\": 1.0\n}\n```"
}
{
  "judge_response": "To evaluate the zero-shot model output, I will assess each key based on its accuracy and relevance to the original review. The scale for rating each key is as follows:\n\n- 0: Completely inaccurate or irrelevant\n- 0.25: Somewhat inaccurate or partially relevant\n- 0.5: Moderately accurate but lacks detail or clarity\n- 0.75: Mostly accurate with minor issues\n- 1: Completely accurate and relevant\n\nNow, I will evaluate each part of the output:\n\n1. **Categories**:\n   - The categories provided (division, department, class, Mt Category) accurately reflect the information in the original prompt. The \"Mt Category\" as \"Apparel\" is a reasonable classification for the context.\n   - **Rating**: 1.0\n\n2. **Sentiment**:\n   - The sentiment is labeled as \"neutral.\" However, the review contains mixed feelings: the reviewer appreciates the uniqueness and improved fit after washing but also expresses concern about the unraveling edges. This suggests a more nuanced sentiment that could lean towards negative due to the concern raised.\n   - **Rating**: 0.5\n\n3. **Summary**:\n   - The summary captures the essence of the review well, noting the uniqueness of the top, the improved fit after washing, and the concern about unraveling edges. It is concise and accurately reflects the main points of the review.\n   - **Rating**: 1.0\n\n4. **Personalized Message**:\n   - The personalized message is appropriate and acknowledges the feedback positively. It addresses both the improved fit and the issue with frayed edges, which shows attentiveness to the customer's experience. However, it could be slightly more empathetic regarding the concern raised.\n   - **Rating**: 0.75\n\n5. **Retail Insight**:\n   - The retail insight suggests improving quality control for frayed edges, which is a relevant and actionable recommendation based on the review. It directly addresses the concern raised by the customer and aims to enhance customer satisfaction.\n   - **Rating**: 1.0\n\nNow, compiling the evaluations into a dictionary format:\n\n```json\n{\n  \"categories\": 1.0,\n  \"sentiment\": 0.5,\n  \"summary\": 1.0,\n  \"personalized_message\": 0.75,\n  \"retail_insight\": 1.0\n}\n```"
}

import json
import re
import pandas as pd # Ensure pandas is imported if not already

# Helper function to extract descriptive text for each category
def extract_description_for_category(full_response_text, start_pattern, end_pattern):
    # Regex to find the text between start_pattern and end_pattern
    # Using re.escape for special characters in patterns and re.DOTALL for multiline match
    match = re.search(re.escape(start_pattern) + r'\s*\n(.*?)\n\s*' + re.escape(end_pattern), full_response_text, re.DOTALL)
    if match:
        description = match.group(1).strip()
        # Remove the "**Rating**: X.X" line from the end of the description
        description = re.sub(r'\s*\*\*Rating\*\*:\s*\d+\.\d+', '', description).strip()
        return description
    return "Description not found"

# Define the patterns for each section (start and end markers)
patterns_to_extract = [
    ("Categories_Description", "1. **Categories**:", "2. **Sentiment**:"),
    ("Sentiment_Description", "2. **Sentiment**:", "3. **Summary**:"),
    ("Summary_Description", "3. **Summary**:", "4. **Personalized Message**:"),
    ("Personalized_Message_Description", "4. **Personalized Message**:", "5. **Retail Insight**:"),
    ("Retail_Insight_Description", "5. **Retail Insight**:", "Overall Evaluation:"), # Note: Adjusted end pattern to capture until "Overall Evaluation"
]


eval_results_parsed_v2 = []
descriptive_results_v2 = [] # New list for descriptive text

for index, row in zero_shot_results_df.iterrows():
  evaluation_result_for_row = llm_as_judge(
    llm_output_to_judge = row['raw_llm_output'],
    original_user_prompt_string_from_zero_shot= row['original_user_content']
  )

  judge_response_str = evaluation_result_for_row['judge_response']

  # Extract numerical scores
  match_scores = re.search(r'```json\n(.*?)```', judge_response_str, re.DOTALL)
  if match_scores:
      json_str = match_scores.group(1)
      try:
          scores = json.loads(json_str)
          eval_results_parsed_v2.append(scores)
      except json.JSONDecodeError:
          print(f"Could not decode JSON for scores from: {json_str}")
          eval_results_parsed_v2.append({}) # Append empty dict on error
  else:
      print(f"No JSON block found for scores in: {judge_response_str}")
      eval_results_parsed_v2.append({}) # Append empty dict if no JSON block

  # Extract descriptive text
  extracted_descriptions = {}
  for name, start_pat, end_pat in patterns_to_extract:
      extracted_descriptions[name] = extract_description_for_category(judge_response_str, start_pat, end_pat)
  descriptive_results_v2.append(extracted_descriptions)

# Create DataFrame from numerical scores (as before)
eval_df_v2 = pd.DataFrame(eval_results_parsed_v2)

# Create a new DataFrame from descriptive texts
eval_df_descriptive_v2 = pd.DataFrame(descriptive_results_v2)

# Display the DataFrame with numerical scores
print("\nNumerical Scores for Zero-Shot V2 (Head):")
display(eval_df_v2.head())
print("\nAverage Numerical Scores for Zero-Shot V2:")
print(eval_df_v2.mean())

# Display the DataFrame with descriptive texts
print("\nDescriptive Explanations for Zero-Shot V2 (Head):")
display(eval_df_descriptive_v2.head())

Numerical Scores for Zero-Shot V2 (Head):

Average Numerical Scores for Zero-Shot V2:
categories              0.993
sentiment               0.940
summary                 0.981
personalized_message    0.931
retail_insight          0.961
dtype: float64

Descriptive Explanations for Zero-Shot V2 (Head):

# Display the DataFrame with numerical scores
print("\nNumerical Scores for Zero-Shot V2 (Head):")
display(eval_df_v2.head(20))
print("\nAverage Numerical Scores for Zero-Shot V2:")
print(eval_df_v2.mean())

# Display the DataFrame with descriptive texts
print("\nDescriptive Explanations for Zero-Shot V2 (Head):")
display(eval_df_descriptive_v2.head(20))

Numerical Scores for Zero-Shot V2 (Head):

Average Numerical Scores for Zero-Shot V2:
categories              0.993
sentiment               0.940
summary                 0.981
personalized_message    0.931
retail_insight          0.961
dtype: float64

Descriptive Explanations for Zero-Shot V2 (Head):

SYSTEM_MESSAGE_CS1 = """
You are a strict, expert retail product review analyst. Prepare a short report
with the following five elements, outputting the response strictly in JSON format:
  1. The categories: this should be an object with three entries: 'division', 'department', 'class' and 'My Category'.
  2. The sentiment: this should be a string indicating 'positive', 'neutral', or 'negative'. If more than one sentiment applies, prioritize the dominant one.
  3. The summary: this is a short summary of the review.
  4. The personalized_message: a personalized message composed in response to the review.
  5. The retail_insight: any insights that can improve customer satisfaction.
"""

FEW_SHOT_MESSAGE_CS1 = [
    {"role": "user", "content": "Division: General\nDepartment: Tops\nClass: Knits\nReview: This pullover is super soft and stylish. It fits perfectly and I always get compliments."},
    {"role": "assistant", "content": """
{
  "categories": {
    "division": "General",
    "department": "Tops",
    "class": "Knits",
    "My Category": "Fit"
  },
  "sentiment": "positive",
  "summary": "The reviewer appreciates the softness and stylish design of the pullover, noting its accurate fit and receiving compliments on it.",
  "personalized_message": "Thank you for your wonderful feedback! We're thrilled to hear that you love the softness and style of your new pullover. Enjoy wearing it!",
  "retail_insight": "Consider highlighting the softness and stylish design in marketing materials, as these features resonate well with customers. Additionally, showcasing customer fit examples could help potential buyers make informed decisions."
},


    {
  "categories": {
    "division": "General",
    "department": "Dresses",
    "class": "Dresses"
    "My Category": "Bunching
  },
  "sentiment": "negative",
  "summary": "The review indicates that the lower part of the dress bunches where the buttons are located, suggesting a design flaw.",
  "personalized_message": "Thank you for your feedback regarding the dress. We apologize for the inconvenience caused by the bunching at the lower part. Your input is valuable, and we will take it into consideration for future designs.",
  "retail_insight": "Consider reviewing the design and fit of the dress, particularly around the button area, to prevent bunching and improve overall customer satisfaction."
}

"""}

]


BASE_MESSAGES_CS1 = [
    {"role": "system", "content": SYSTEM_MESSAGE_CS1}
]

#imput for llm-as-a-judge
def review_prompt_five3():
    """
    Samples five reviews and returns them as a list of dictionaries,
    each representing a single review.
    """
    five_samples_df = reviews4.sample(n=50) # Get five random rows as a DataFrame
    list_of_review_dicts = []
    for index, row in five_samples_df.iterrows():
        list_of_review_dicts.append({
            "div_name": row['Division.Name'],
            "dept_name": row['Department.Name'],
            "class_name": row['Class.Name'],
            "review_text": row['Review.Text'],
            "review_index": row['index']
        })
    return list_of_review_dicts

def few_shot_prompt(model_name: str = "gpt-4o-mini", review_data1: dict = None) -> str:
    """
    Generates a Few-Shot prompt response based on the provided review data.
    """
    if review_data1 is None:
        review_data1 = review_prompt_one3()

    user_content1 = (
        f"Division: {review_data1['div_name']}\n"
        f"Department: {review_data1['dept_name']}\n"
        f"Class: {review_data1['class_name']}\n"
        f"Review: {review_data1['review_text']}"
        f"Index: {review_data1['review_index']}"
    )

    instructions = BASE_MESSAGES_CS1 + [{
        "role": "user",
        "content": user_content1
    }]

    response = client.chat.completions.create(
        model=model_name,
        messages=instructions,
        temperature=0.0,
        max_tokens=200
    )

    raw_info1 = response.choices[0].message.content.strip()
    #print("This is the actual review:\n", review_data1['review_text'], '\n\n')

    return {'raw_info1':raw_info1}, {'user_content1': user_content1}, review_data1['review_index'] # Return raw_info dict, user_content string, and review_index

# Call review_prompt_five3() to get a list of five review samples
list_of_review_samples1 = review_prompt_five3()

# Process each review sample with zero_shot_prompt and collect outputs
llm_outputs_parsed_list1 = [] # Changed name to be more descriptive
for sample_data1 in list_of_review_samples1:
    raw_info_dict1, user_content_dict1, review_idx1 = few_shot_prompt(review_data1=sample_data1)
    llm_outputs_parsed_list1.append({
        "raw_llm_output1": raw_info_dict1['raw_info1'],
        "original_user_content1": user_content_dict1['user_content1'],
        "review_index": review_idx1
    })

# Create a DataFrame from the parsed outputs
few_shot_results_df1 = pd.DataFrame(llm_outputs_parsed_list1)
few_shot_results_df1.head()

This is the actual review:
 This is an awesome vest - so soft, cozy, and i cannot wait to wear it through fall and winter. for sake of not repeating all the positive aspects that the previous reviewers did, i'll mention the one flaw...no pockets :( still totally worth full price in my mind though. 


This is the actual review:
 Always attracted to wool for its warmth and breathability. this jacket has great style that can be casual or dressed up some. it's slightly more boxy then expected which i think lends to the casual look. find myself wearing it a lot. 


This is the actual review:
 Tts. i'm 38c, 8-10 and m fits just right. nice and fitted at the top, flowy skirt. i got the cream and love the skirt pattern. 


This is the actual review:
 I chose to ignore the other reviews saying this shirt was too wide thinking that my wider shoulders would fill it out, but that's not the case. the shirt is adorable and soft and cute on, but just too wide around the armpit/chest area and with the horizontal stripes it just didn't look attractive. i would love to try the next size down to see if it works, but the black and white xxs is sold out so back it went. i am only 5'4" with a shorter torso so the length was fine for me. 


This is the actual review:
 This is a beautiful sweater with deep rich colors. i purchased the blue motif. the problem is that the model(s) are small busted, which allows the side panels and front panel to lay nicely and swing in a flattering manner. i am 5'6'', 135 pounds and a 36d. i usually wear a small in antro tops. i purchased this online, and was excited upon it's arrival. when i tried it on, i was horrified. it looked like a babydoll maternity sweater on me. the front did not lay well (flat) at all from the chest d 


This is the actual review:
 The material is a medium weight jersey knit that is slightly thicker so it provides enough coverage and support to be flattering without being bulky. i think this dress is true-to-size, or maybe runs a little bit big, but only by a little bit. i bought a medium and am a size 8 in tops and 10 in pants. the dress hugs nicely on the upper body (arms, chest, ribs) and flares just a little bit on the hips/tummy/butt area. it is forgiving of little bumps or lumps. the scoop neckline is a bit lower tha 


This is the actual review:
 Love this! i am 5' 118 and the medium fits like a dress, not a tunic. i opted for the medium because i didn't want the fit to be tight. it is thin so you'll need a slip. 


This is the actual review:
 I ordered this item when it was first advertised, fell in love with the unique pattern in the top and could hardly wait for it to get here. however when i received the top i was very disappointed, the fabric is cheap, the sleeves are tight and the overall product is of poor quality and poorly constructed. needless to say i returned it. 


This is the actual review:
 This top is really pretty. it's a very strong embroidered lace all over (front and back) with small holes. but, the description states there is a polyester lining, and this is unlined! so,there are holes. but, i do not think this is immodest and needs a cami. just wear a nude bra under it. the holes are not revealing at all.

the sizing is tts, and is as shown in the photo. i bought an xl because if a top isn't stretchy i need to wear a 16 or 18. this fits me, only it's not as loose in the bac 


This is the actual review:
 This design looks really sharp on the image, but in reality this jacket felt more like a jacket you would get at forever xxi.  the fabric had that polyester/shiny quality to it that made it look cheap.  since the fabric was pretty soft, with a gray jersey lining, it felt more like a padded jacket than a constructed blazer.  

i also thought the cut was small if you have a bust larger than a b, i felt like i was busting out of a size 8, and i am a 34d. 


This is the actual review:
 This romper is adorable!!! i love the long sleeves! 


This is the actual review:
 I ordered a m/l in this vest and it fits perfectly. thirst is cozy and stylish. in fact, i ordered the same vest in another color! it's versatile enough to wear with both skirts and jeans. i also think it's a timeless piece that will stay in style for years to come. 


This is the actual review:
 I saw this dress online first and suspected i would like it, but the price scared me away. then, while visiting in portland, i saw it on sale at an retailer store. when i came out of the dressing room to look in a bigger mirror the store clerk said, "well that's a no-brainer!" i thought the same thing. i love this dress so much i actually would have paid full price for it. it's super fun and easy to wear; i paired it with navy blute tights and suede ankle booties. i'm 5'6, 138 lbs and the m 


This is the actual review:
 As others have said, this dress does run large. i ordered the xxs petite, and ti isalmsot big on me (hips are wide, so at least, it is comfortable in width). material is a bit scratchier than i am used to s well.

color is great, and it looks nice when i see it on my stylist, who convinced me to hold on to it for a little... thinking about it.

my zipepr was also a little hard to work.

shorter than some other dresses... 


This is the actual review:
 I'm so happy i decided to take a chance and order this top. it is gorgeous! so flattering on, the perfect length, beautiful colors. i could go on and on. peplums can be tough but this one hits perfectly on me. runs true to retailer sizing so stick with what you normally order. i'm 5'6" and 125lb and the s was for me. 


This is the actual review:
 So sad that this cardigan did not fit me. i listened to the review i read and tried a small, didn't come close to fitting. i tried a medium and it also did not fit. i think this item is designed for someone who has a small frame. i seem to be too tall for the item and too big in the chest. i usually always fit into a size medium.that being said the cardigan is very pretty and feminine, has an interesting design and is well made. so sad this item didn't work for me :-( 


This is the actual review:
 This dress seems quite high quality and is wonderfully designed, except in the chest. the wrap style hugs in all the right places but the chest. i'm slightly large chested (34c/d) but can usually wear a size small or size 4 in dresses and tops (5'5", 122 lbs). the small in this dress was great everywhere else, but i couldn't breathe and it totally flattened out my chest. also, it is quite low cut, so another mark against it for anyone with a semi-large bust. couldn't wear it to work that reason 


This is the actual review:
 Great petite maxi! i'm 5'3 and it skims the top of my feet. soft, fits great and went right into my suitcase for my upcoming  trip. can dress up or down. 


This is the actual review:
 Perfect dress for the fall in texas. the velvet is on point for this season. the dress is very roomy on the bottom half...wish it was a little more fitted. but, overall a great dress, got tons of compliments! 


This is the actual review:
 Not a huge fan of this dress, will be returning it.
fits too big and not flattering.
fabric is thin and shows any lumps and bumps you may have.
color is not as pretty in person as in the photo - the photo looks like a nice rust color but the dress is actually a boring brownish olive. 


This is the actual review:
 Byron lars knows how to make you look like a woman with his magical dresses!  i decided to get the navy (i also own the sold-out marguerite aka carissima in bronze) instead of the black or white since everyone and their mother owns it already.  the lace of the top is different--more delicate but less intricate.  it's still very beautiful.    my body type is inverted and the dress is hourglass, so the size 8p fits great on top but loose on my hips and rear.  i'm going to keep this dress and do mo 


This is the actual review:
 The torso runs a bit long, but this is otherwise a comfortable, and cute dress. 


This is the actual review:
 Love the design with the buttons down the back but unfortunately this did not work for me. the colors were brighter than shown on the website which was my first disappointment, i prefer more muted colors. the second disappointment was the material. for me it was just too itchy. unfortunately, this one's going back. 


This is the actual review:
 This shirt looks nothing like the fit in the pictures. it is short and very wide. i had read the previous reviews and thought i could take it in on the sides some but it is literally the shape of a trapezoid. 


This is the actual review:
 Beautiful flattering blouse 


This is the actual review:
 I like that i can wear this over anything, only drawback is that it's short-sleeved so it semi-defeats the purpose of having a cardigan but- it's so pretty i can't return it! 


This is the actual review:
 Beautiful lace detail and very soft material. runs a little big. 


This is the actual review:
 Absolutely in love with this dress! perfect to wear to work or for a casual brunch or cookout. i'm a 34b, 27" waist, and 36" hip and 5'3" and went with the xsp. very impressed with the quality and well worth the sale price. 


This is the actual review:
 I really wanted this skirt to work but it didn't look very flattering on me. a great midi length and very cool pattern, so hopefully will work for others! 


This is the actual review:
 Had high hopes for this seemingly cute skirt to use as a basic in my spring and summer outfits. however, that was not the case. this skirt is not lined and the material is so so. basically, for the price, i was underwhelmed and will return it. 


This is the actual review:
 This reminded me of big bird in person and just adds volume to your body. i'm thin and usually wear a size 0, so i'm not sure who this may look good on. 


This is the actual review:
 I haven't gotten a dress from retailer in a long time that i like as i do this one! this is the old retailer-quality, extras, and unique design! fits like it was made for me-tts, i am 5'5", 142 lbs and the 6 is perfection! no enlarged arm holes or funky fit...it has lining as well as a tu-tu slip underneath so you can create your own fullness in the skirt. it is very fitted at the bust and waist and will hide numerous flaws at the hips. this dress is spectacular so don't wait...press the button!!!! 


This is the actual review:
 I bought this dress in the red option. was a little concerned when i read other reviews that said it looked like a maternity dress, but it didn't look like that on me at all. it falls nicely and fits me well - i am 5 ft, 107 lbs - in size xs. love the details at the neckline and hem. will be looking out for other dresses by maeve! 


This is the actual review:
 These leggings are super comfy. i took the advice of other reviewers and ordered a size down and the leggings fit wonderfully. lately i've been having a problem with leggings falling down at the waist and these stay up nicely so i was very pleased. the leggings have a slight itch because of the sweater feel but they are very warm and i enjoy wearing them. 


This is the actual review:
 The print is unique and fun. however, the longer length and heavier weight of the fabric was not the piece i needed in my closet. 


This is the actual review:
 Really cute top(especially the back)!  i've tried on several of these tops that overlap in the back and none of them lay as nice as this one.  the soft cotton fabric really works well with this style.  runs tts and i love that the arms are more fitted with some stretch to the fabric.  regular length works fine on my petite frame as well.  the color is a darker cream and almost has a slight nubby effect to it.  first one i received had a snag/hole in the lower sleeve, so hopefully it holds up. 


This is the actual review:
 These are great. they fit tts - i'm a 26 in most denim brands & that's the size i got. the material has some give but doesn't stretch out. the color is not super white so you can actually wear them in the colder weather season as well. at 5'4", they were a little longer on me than shown on the model. i find them very comfortable. 


This is the actual review:
 The design is cute, but there is way too much fabric in the lower part of the shirt. also, the fabric for the lower part crumples easily. unless you carry around an iron, you may not want to wear it outside the house. i kept the shirt to wear around the house. 


This is the actual review:
 I loved this blouses when i saw it on-line, but when i saw it in on me it looked totally different. it runs really large and i didn't like the material of blouse. 


This is the actual review:
 This is a great little top. the tie sits right at the waist and loose enough to slide over top of pants or shorts. the detailing at neck and shoulders is very eye catching. i received several compliments when i wore it. 


This is the actual review:
 I bought this shirt in a size small. it fits the same as it does on the model. it's a comfortable cute shirt. wish the other colors were available because i'd order another one. 


This is the actual review:
 Love this sweater! it can be used for both work and casual wear with ease. very soft and seems durable. i highly recommend it! 


This is the actual review:
 I love this top so much i bought it in 3 colors. the material is so soft and comfortable. has a slight fitting in the waist to make it very flattering. you can wear the next as pictured or button it up and slouch it and it looks great both ways. perfect with jeans or dress it up with slacks. just wish it wasn't dry clean only. 


This is the actual review:
 I am so pleased with this tunic. the fabric heavy enough to wear through winter, and it is very flattering. the colors are as pictured, as well. as another reviewer mentioned, the slit is high. but it still works perfectly with leggings or mid to high rise skinny jeans. 


This is the actual review:
 Got this dress on sale. it fits great. i am 5'8", small bust, small waist but bigger hips. and this dress makes all those areas look great. 


This is the actual review:
 Fun top, 5'4, 135 lbs and i ordered xs. it was too wide and boxy so exchanged for xxs and it fits great. i like the fun slant to the side. not tight on stomach which is great! :) great top. 


This is the actual review:
 I am on the fence about this one. the length is great. it hits right above my knee at 5'1. overall it was larger than i was hoping. the waist is pretty loose, and i really had to tightly tie the back to get s good shoulder fit. the bottom is very voluminous making me look wide. i wish they had it in a petite size. for this price, i will probably return it. it's not worth the 300 price tag. 


This is the actual review:
 I love pilcro jeans and usually wear the stet fit. this is the first pair that i've owned that had so much stretch. they stretch and do lose their shape slightly, but bounce back after a wash. my main complaint may be stretch-related: after the first wear, the inner thigh area was covered in pills. granted, my inner thighs touch when i walk, but this has never happened with any other pair of pilcro that i've owned. i still have a pair of black stet that i bought 4yrs ago and they've never shown 


This is the actual review:
 I love wearing sweaters when it gets cooler out but hate the bulkiness that comes with them. this is a super cute sweater that isn't too bulky and gives you the sexiness you're looking for! great material. 


This is the actual review:
 Very nice fabric but disappointed in the stitching on the knees. it looks nice but makes the pants very uncomfortable and tight around the knees. too bad because i love the style and hoped they would work!

few_shot_results_df1.head(20)

import json

def parse_llm_output1(raw_output_str1):
    # The LLM output is wrapped in ```json\n...\n```, so we need to extract the inner JSON string
    match = re.search(r'```json\n(.*)```', raw_output_str1, re.DOTALL)
    if match:
        json_str1 = match.group(1).strip()
        try:
            return json.loads(json_str1)
        except json.JSONDecodeError:
            print(f"JSON Decode Error for: {json_str1[:100]}...")
            return None
    return None

# Apply the parsing function to the 'raw_llm_output' column
few_shot_results_df1['parsed_output1'] = few_shot_results_df1['raw_llm_output1'].apply(parse_llm_output1)

# Extract individual fields into new columns
few_shot_results_df1['extracted_categories'] = few_shot_results_df1['parsed_output1'].apply(lambda x: x.get('categories') if x else None)
few_shot_results_df1['extracted_sentiment'] = few_shot_results_df1['parsed_output1'].apply(lambda x: x.get('sentiment') if x else None)
few_shot_results_df1['extracted_summary'] = few_shot_results_df1['parsed_output1'].apply(lambda x: x.get('summary') if x else None)
few_shot_results_df1['extracted_personalized_message'] = few_shot_results_df1['parsed_output1'].apply(lambda x: x.get('personalized_message') if x else None)
few_shot_results_df1['extracted_retail_insight'] = few_shot_results_df1['parsed_output1'].apply(lambda x: x.get('retail_insight') if x else None)

# Display the DataFrame with the new columns
display(few_shot_results_df1.head(20))

SYSTEM_MESSAGE_CS21 = """
You are a large-language-model-as-a-judge for other large-language-models, to
check their accuracy and the quality of their output.

Rules:
- Return an evaluation of the zero-shot model output, by creating a dict with
the following keys: "categories", "sentiment", "summary", "personalized_message",
and "retail_insight".
-create a separate rating for each key/value from the review
on a scale from zero to one. Decimals between zero and one
are allowed. Explain the scale then rate each part.
- Compare your value of the sentiment and the sentiment polarity value from the
textblob evaluation of the review.
"""

BASE_MESSAGES_CS21 = [
    {"role": "system", "content": SYSTEM_MESSAGE_CS20},
]

def llm_as_judge_few(model_name: str = "gpt-4o-mini", llm_output_to_judge1: str = None, original_user_prompt_string_from_few_shot1: str = None) -> dict:
    """
    Acts as an LLM-as-a-judge to score the quality of model outputs.
    It takes the LLM's generated output string, the original review data dictionary,
    and the exact user prompt string that was sent to the zero-shot model.
    """

    if llm_output_to_judge1 is None or original_user_prompt_string_from_few_shot1 is None:
       return {"score1": 0.0, "reason1": "Missing input for judging"}

    # Use the pre-formatted user_content string from the zero-shot prompt
    # and add the LLM's output for the judge to evaluate
    user_content_for_judge1 = (
        f"Original Prompt to Zero-Shot LLM:\n{original_user_prompt_string_from_few_shot1}\n\n"
        f"Few-Shot LLM Output to Evaluate:\n{llm_output_to_judge1}\n\n"
        f"Evaluate the Zero-Shot LLM Output based on the provided Original Prompt.\n"
        f"Ensure your evaluation focuses on the accuracy and completeness of the categories, sentiment, summary, personalized message, and retail insight given the original review text in the prompt."
    )

    instructions = BASE_MESSAGES_CS20 + [{
        "role": "user",
        "content": user_content_for_judge1
    }]

    response = client.chat.completions.create(
        model=model_name,
        messages=instructions,
        temperature=0.0,
        max_tokens=500
    )

    judge_raw_output1 = response.choices[0].message.content.strip()

    return {"judge_response1": judge_raw_output1}

eval1 = []
for index1, row in few_shot_results_df1.iterrows():
  evaluation_result_for_row1 = llm_as_judge_few(
    llm_output_to_judge1 = row['raw_llm_output1'],
    original_user_prompt_string_from_few_shot1= row['original_user_content1']
  )
  eval1.append(json.dumps(evaluation_result_for_row1, indent=2))

# Print all evaluations collected
for eval_result1 in eval1:
  print(eval_result1)

{
  "judge_response1": "To evaluate the zero-shot model output, I will assess each key based on its accuracy and completeness in relation to the original review. The scale for rating each part is as follows:\n\n- 0: Completely inaccurate or irrelevant\n- 0.25: Somewhat inaccurate or missing key elements\n- 0.5: Partially accurate but lacks detail or clarity\n- 0.75: Mostly accurate with minor issues\n- 1: Completely accurate and comprehensive\n\nNow, I will rate each part of the output:\n\n1. **Categories**:\n   - The categories provided (division, department, class, and My Category) accurately reflect the information in the original review. The addition of \"My Category\" as \"Apparel\" is appropriate and relevant.\n   - **Rating**: 1.0\n\n2. **Sentiment**:\n   - The sentiment is correctly identified as \"positive,\" which aligns with the overall tone of the review. The reviewer expresses enthusiasm and satisfaction with the product.\n   - **Rating**: 1.0\n\n3. **Summary**:\n   - The summary captures the main points of the review, highlighting the love for the vest's softness and coziness while also mentioning the flaw of lacking pockets. It is concise and accurately reflects the review's content.\n   - **Rating**: 1.0\n\n4. **Personalized Message**:\n   - The personalized message is warm and acknowledges the reviewer's positive feedback. It also addresses the concern about the lack of pockets, which shows attentiveness to customer feedback. However, it could be slightly more personalized by mentioning the specific product (the vest).\n   - **Rating**: 0.75\n\n5. **Retail Insight**:\n   - The retail insight is practical and directly addresses the flaw mentioned in the review. It suggests a potential improvement for future designs, which is relevant and actionable. This insight aligns well with customer preferences.\n   - **Rating**: 1.0\n\nBased on the evaluations, here is the final output:\n\n```json\n{\n  \"categories\": 1.0,\n  \"sentiment\": 1.0,\n  \"summary\": 1.0,\n  \"personalized_message\": 0.75,\n  \"retail_insight\": 1.0\n}\n```"
}
{
  "judge_response1": "To evaluate the zero-shot model output, I will assess each key based on its accuracy and completeness in relation to the original review provided. The scale for rating will be as follows:\n\n- 0: Completely inaccurate or irrelevant\n- 0.25: Somewhat inaccurate or lacking detail\n- 0.5: Partially accurate but missing key elements\n- 0.75: Mostly accurate with minor issues\n- 1: Completely accurate and comprehensive\n\nNow, I will evaluate each part of the output:\n\n1. **Categories**:\n   - The categories provided (division, department, class, and My Category) are accurate and relevant to the original review. The addition of \"Wool Jackets\" as a category is appropriate given the review's focus on a wool jacket.\n   - **Rating**: 1.0\n\n2. **Sentiment**:\n   - The sentiment is correctly identified as \"positive,\" reflecting the reviewer's appreciation for the jacket's warmth, style, and versatility.\n   - **Rating**: 1.0\n\n3. **Summary**:\n   - The summary accurately captures the main points of the review, including the warmth, breathability, style, versatility, and casual fit of the jacket. It effectively conveys the reviewer's overall positive experience.\n   - **Rating**: 1.0\n\n4. **Personalized Message**:\n   - The personalized message is thoughtful and acknowledges the reviewer's feedback on warmth and style. It also expresses gratitude, which is appropriate for customer engagement. However, it could have included a reference to the boxy fit mentioned in the review for a more tailored response.\n   - **Rating**: 0.75\n\n5. **Retail Insight**:\n   - The retail insight suggests providing more detailed sizing information and fit descriptions, which is relevant given the reviewer's comment about the jacket being \"slightly more boxy than expected.\" This insight is actionable and aligns well with the review.\n   - **Rating**: 1.0\n\nBased on the evaluations, here is the final output:\n\n```json\n{\n  \"categories\": 1.0,\n  \"sentiment\": 1.0,\n  \"summary\": 1.0,\n  \"personalized_message\": 0.75,\n  \"retail_insight\": 1.0\n}\n```"
}
{
  "judge_response1": "To evaluate the zero-shot model output, I will assess each key based on its accuracy and completeness in relation to the original review provided in the prompt. The scale for rating will be as follows:\n\n- 0: Completely inaccurate or irrelevant\n- 0.25: Somewhat inaccurate or missing key elements\n- 0.5: Partially accurate but lacks detail or clarity\n- 0.75: Mostly accurate with minor issues\n- 1: Completely accurate and comprehensive\n\nNow, I will rate each part of the output:\n\n1. **Categories**:\n   - The categories correctly reflect the division, department, and class as \"General,\" \"Dresses,\" and \"Dresses,\" respectively. The additional \"My Category\" is redundant but does not detract from the accuracy.\n   - **Rating**: 1.0\n\n2. **Sentiment**:\n   - The sentiment is accurately identified as \"positive,\" which aligns with the reviewer's satisfaction with the fit and design of the dress.\n   - **Rating**: 1.0\n\n3. **Summary**:\n   - The summary captures the key points of the review, including the fit, the flowy skirt, and the color. It effectively conveys the reviewer's positive experience.\n   - **Rating**: 1.0\n\n4. **Personalized Message**:\n   - The personalized message is appropriate and expresses gratitude for the feedback. It reflects the positive sentiment of the review and acknowledges the specific aspects the reviewer enjoyed.\n   - **Rating**: 1.0\n\n5. **Retail Insight**:\n   - The retail insight provides a valuable suggestion to highlight fit and design features in marketing materials, which is relevant to the review. It aligns well with the reviewer's emphasis on fit and design.\n   - **Rating**: 1.0\n\nBased on the evaluations, here is the final output:\n\n```json\n{\n  \"categories\": 1.0,\n  \"sentiment\": 1.0,\n  \"summary\": 1.0,\n  \"personalized_message\": 1.0,\n  \"retail_insight\": 1.0\n}\n```"
}
{
  "judge_response1": "To evaluate the zero-shot model output, I will assess each key based on its accuracy and relevance to the original review. The scale for rating each key is as follows:\n\n- 0: Completely inaccurate or irrelevant\n- 0.25: Somewhat inaccurate or partially relevant\n- 0.5: Neutral; neither accurate nor inaccurate\n- 0.75: Mostly accurate with minor issues\n- 1: Completely accurate and relevant\n\nNow, I will rate each part of the output:\n\n1. **Categories**:\n   - The categories provided (division, department, class, and My Category) accurately reflect the context of the review. The \"My Category\" as \"Apparel\" is a reasonable generalization.\n   - **Rating**: 1.0\n\n2. **Sentiment**:\n   - The sentiment is labeled as \"neutral.\" However, the review expresses a mix of positive feelings about the shirt's softness and cuteness, alongside negative feelings regarding the fit. A more accurate sentiment might be \"mixed\" or \"negative\" due to the emphasis on the fit issues.\n   - **Rating**: 0.5\n\n3. **Summary**:\n   - The summary captures the main points of the review well, highlighting both the positive aspects (cute and soft) and the negative aspect (too wide around the armpit/chest area). It accurately reflects the reviewer's experience.\n   - **Rating**: 1.0\n\n4. **Personalized Message**:\n   - The personalized message is well-crafted, acknowledging the reviewer's feedback and expressing understanding of their fit issues. It also encourages the reviewer to try a smaller size when available, which is appropriate and thoughtful.\n   - **Rating**: 1.0\n\n5. **Retail Insight**:\n   - The retail insight suggests offering a wider range of sizes and fit options, which is a relevant and constructive suggestion based on the review. It also mentions providing detailed sizing guides, which aligns with the reviewer's experience of fit issues.\n   - **Rating**: 1.0\n\nBased on the evaluations, here is the final output:\n\n```json\n{\n  \"categories\": 1.0,\n  \"sentiment\": 0.5,\n  \"summary\": 1.0,\n  \"personalized_message\": 1.0,\n  \"retail_insight\": 1.0\n}\n```"
}
{
  "judge_response1": "To evaluate the zero-shot model output, I will assess each key based on its accuracy and completeness in relation to the original review. The scale for rating will be as follows:\n\n- 0: Completely inaccurate or irrelevant\n- 0.25: Somewhat inaccurate or missing key elements\n- 0.5: Partially accurate but lacks depth or clarity\n- 0.75: Mostly accurate with minor issues\n- 1: Completely accurate and comprehensive\n\nNow, I will rate each part of the output:\n\n1. **Categories**:\n   - The categories provided (division, department, class, and an additional category \"My Category\") are accurate and relevant to the review. The classification aligns well with the content of the review.\n   - **Rating**: 1.0\n\n2. **Sentiment**:\n   - The sentiment is correctly identified as \"negative,\" which reflects the customer's disappointment with the fit of the sweater despite initially liking its appearance.\n   - **Rating**: 1.0\n\n3. **Summary**:\n   - The summary accurately captures the essence of the review, highlighting both the positive aspect (the beauty of the sweater) and the negative aspect (the fit issue related to the customer's bust size). It succinctly conveys the customer's experience.\n   - **Rating**: 1.0\n\n4. **Personalized Message**:\n   - The personalized message is appropriate and empathetic, acknowledging the customer's feedback and expressing regret that the fit did not meet expectations. It also shows appreciation for the insights provided, which is a good customer service approach.\n   - **Rating**: 1.0\n\n5. **Retail Insight**:\n   - The retail insight is practical and relevant, suggesting a wider range of sizes and fit options, as well as the importance of detailed fit guides. This aligns well with the customer's feedback about fit issues and could help improve future customer experiences.\n   - **Rating**: 1.0\n\nBased on the evaluations, here is the final output:\n\n```json\n{\n  \"categories\": 1.0,\n  \"sentiment\": 1.0,\n  \"summary\": 1.0,\n  \"personalized_message\": 1.0,\n  \"retail_insight\": 1.0\n}\n```"
}
{
  "judge_response1": "To evaluate the zero-shot model output, I will assess each key based on its accuracy and completeness in relation to the original review. The scale for rating each key is as follows:\n\n- 0: Completely inaccurate or irrelevant\n- 0.25: Somewhat inaccurate or missing key elements\n- 0.5: Partially accurate but lacks detail or clarity\n- 0.75: Mostly accurate with minor issues\n- 1: Completely accurate and comprehensive\n\nNow, I will rate each part of the output:\n\n1. **Categories**:\n   - The categories provided (division, department, class, My Category) accurately reflect the context of the review, which is about a dress. The addition of \"My Category\" as \"Apparel\" is appropriate and relevant.\n   - **Rating**: 1.0\n\n2. **Sentiment**:\n   - The sentiment is labeled as \"positive,\" which aligns well with the overall tone of the review. The reviewer expresses satisfaction with the dress's fit and material.\n   - **Rating**: 1.0\n\n3. **Summary**:\n   - The summary captures the key points of the review, including the material, fit, and the flattering nature of the dress. It accurately reflects the reviewer's experience and opinion.\n   - **Rating**: 1.0\n\n4. **Personalized Message**:\n   - The personalized message is warm and acknowledges the reviewer's detailed feedback. It expresses gratitude and highlights the usefulness of the insights for other customers, which is appropriate.\n   - **Rating**: 1.0\n\n5. **Retail Insight**:\n   - The retail insight suggests providing a detailed sizing guide and customer feedback on fit, which is relevant given the review mentions sizing. This advice is practical and aligns with the review's content.\n   - **Rating**: 1.0\n\nBased on the evaluations, here is the final output:\n\n```json\n{\n  \"categories\": 1.0,\n  \"sentiment\": 1.0,\n  \"summary\": 1.0,\n  \"personalized_message\": 1.0,\n  \"retail_insight\": 1.0\n}\n```"
}
{
  "judge_response1": "To evaluate the zero-shot model output, I will assess each key based on its accuracy and relevance to the original review. The scale for rating is as follows:\n\n- 0: Completely inaccurate or irrelevant\n- 0.25: Somewhat inaccurate or partially relevant\n- 0.5: Moderately accurate but lacking in detail or clarity\n- 0.75: Mostly accurate with minor issues\n- 1: Completely accurate and relevant\n\nNow, I will rate each part of the output:\n\n1. **Categories**:\n   - The categories provided (division, department, class, and My Category) accurately reflect the information in the original review. The addition of \"My Category\" as \"Apparel\" is appropriate and relevant.\n   - **Rating**: 1.0\n\n2. **Sentiment**:\n   - The sentiment is correctly identified as \"positive,\" which aligns with the reviewer's enthusiastic expression of love for the product.\n   - **Rating**: 1.0\n\n3. **Summary**:\n   - The summary captures the main points of the review, including the fit of the medium size and the note about the thinness requiring a slip. It is concise and accurately reflects the review's content.\n   - **Rating**: 1.0\n\n4. **Personalized Message**:\n   - The personalized message is warm and acknowledges the reviewer's feedback. It also addresses the concern about the thinness, which shows attentiveness to the review. However, it could be slightly more tailored to the specific details of the review.\n   - **Rating**: 0.75\n\n5. **Retail Insight**:\n   - The retail insight suggests offering recommendations for layering options or slips, which is a practical suggestion based on the review. It demonstrates an understanding of customer needs and enhances the shopping experience.\n   - **Rating**: 1.0\n\nNow, I will compile the evaluation into a dictionary format:\n\n```json\n{\n  \"categories\": 1.0,\n  \"sentiment\": 1.0,\n  \"summary\": 1.0,\n  \"personalized_message\": 0.75,\n  \"retail_insight\": 1.0\n}\n```"
}
{
  "judge_response1": "To evaluate the zero-shot model output, I will assess each key based on its accuracy and relevance to the original review. The scale for rating will be as follows:\n\n- 0: Completely inaccurate or irrelevant\n- 0.25: Somewhat inaccurate or partially relevant\n- 0.5: Neutral; neither accurate nor inaccurate\n- 0.75: Mostly accurate but with minor issues\n- 1: Completely accurate and relevant\n\nNow, I will rate each part of the output:\n\n1. **Categories**:\n   - The categories provided (division, department, class, and an additional category \"My Category\") are accurate and align with the original review. The division, department, and class match the context of the review about a blouse.\n   - **Rating**: 1.0\n\n2. **Sentiment**:\n   - The sentiment is correctly identified as \"negative,\" which reflects the customer's disappointment with the product.\n   - **Rating**: 1.0\n\n3. **Summary**:\n   - The summary accurately captures the customer's initial excitement and subsequent disappointment regarding the fabric quality, fit, and construction, leading to a return. It succinctly encapsulates the main points of the review.\n   - **Rating**: 1.0\n\n4. **Personalized Message**:\n   - The personalized message is appropriate and empathetic, acknowledging the customer's feedback and expressing a commitment to improving product quality. It aligns well with the tone of customer service responses.\n   - **Rating**: 1.0\n\n5. **Retail Insight**:\n   - The retail insight provides constructive suggestions for improving product quality and fit specifications, which are directly relevant to the issues raised in the review. It demonstrates an understanding of how to address customer concerns to enhance satisfaction.\n   - **Rating**: 1.0\n\nBased on the evaluations, here is the final output:\n\n```json\n{\n  \"categories\": 1.0,\n  \"sentiment\": 1.0,\n  \"summary\": 1.0,\n  \"personalized_message\": 1.0,\n  \"retail_insight\": 1.0\n}\n```"
}
{
  "judge_response1": "To evaluate the zero-shot model output, I will assess each key based on its accuracy and completeness in relation to the original review. The scale for rating is as follows:\n\n- 0: Completely inaccurate or irrelevant\n- 0.25: Somewhat inaccurate or missing key details\n- 0.5: Partially accurate but lacks depth or clarity\n- 0.75: Mostly accurate with minor issues\n- 1: Completely accurate and comprehensive\n\nNow, I will rate each part of the output:\n\n1. **Categories**:\n   - The categories provided (division, department, class, and an additional category) are accurate and relevant to the review. The addition of \"Embroidered Lace Tops\" is a reasonable interpretation of the product based on the review.\n   - **Rating**: 1.0\n\n2. **Sentiment**:\n   - The sentiment is labeled as \"neutral,\" which is somewhat misleading. The review expresses positive feelings about the top's beauty and fit, despite mentioning a concern about the lining. A more accurate sentiment would be \"positive\" or \"mixed.\"\n   - **Rating**: 0.5\n\n3. **Summary**:\n   - The summary captures the main points of the review, highlighting both the praise for the top and the issue with the lining. However, it could be more concise and clearer in stating that the reviewer does not find the holes revealing.\n   - **Rating**: 0.75\n\n4. **Personalized Message**:\n   - The personalized message is appropriate and acknowledges the reviewer's feedback positively. It addresses the beauty and fit of the top while also recognizing the concern about the lining. However, it could be slightly more tailored to reflect the reviewer's specific comments about the holes not being revealing.\n   - **Rating**: 0.75\n\n5. **Retail Insight**:\n   - The retail insight is practical and directly addresses the issue raised in the review about the product description. It suggests a clear action to improve customer satisfaction, which is relevant and useful.\n   - **Rating**: 1.0\n\nBased on the evaluations, here is the final output:\n\n```json\n{\n  \"categories\": 1.0,\n  \"sentiment\": 0.5,\n  \"summary\": 0.75,\n  \"personalized_message\": 0.75,\n  \"retail_insight\": 1.0\n}\n```"
}
{
  "judge_response1": "To evaluate the zero-shot model output, I will assess each key based on its accuracy and relevance to the original review. The scale for rating each key is as follows:\n\n- 0: Completely inaccurate or irrelevant\n- 0.25: Somewhat inaccurate or lacks relevance\n- 0.5: Moderately accurate but could be improved\n- 0.75: Mostly accurate with minor issues\n- 1: Completely accurate and relevant\n\nNow, I will rate each part of the output:\n\n1. **Categories**:\n   - The categories provided (division, department, class, and My Category) are accurate and relevant to the original review. The addition of \"My Category\" as \"Outerwear\" is appropriate since jackets fall under that category.\n   - **Rating**: 1\n\n2. **Sentiment**:\n   - The sentiment is correctly identified as \"negative,\" which aligns with the reviewer's disappointment regarding the jacket's quality and fit.\n   - **Rating**: 1\n\n3. **Summary**:\n   - The summary captures the main points of the review: the appealing design contrasted with the disappointing quality and fit, particularly for larger bust sizes. It accurately reflects the review's content.\n   - **Rating**: 1\n\n4. **Personalized Message**:\n   - The personalized message is appropriate and empathetic, acknowledging the customer's feedback and expressing a desire to improve. It aligns well with the tone of customer service.\n   - **Rating**: 1\n\n5. **Retail Insight**:\n   - The retail insight is relevant and actionable, suggesting improvements in fabric quality and size range, which directly addresses the concerns raised in the review. It shows an understanding of customer needs.\n   - **Rating**: 1\n\nBased on the evaluations, here is the final output:\n\n```json\n{\n  \"categories\": 1,\n  \"sentiment\": 1,\n  \"summary\": 1,\n  \"personalized_message\": 1,\n  \"retail_insight\": 1\n}\n```"
}
{
  "judge_response1": "To evaluate the zero-shot model output, I will assess each key based on its accuracy and completeness in relation to the original review. The scale for rating each key is as follows:\n\n- 0: Completely inaccurate or irrelevant\n- 0.25: Somewhat inaccurate or missing key elements\n- 0.5: Partially accurate but lacks detail or clarity\n- 0.75: Mostly accurate with minor issues\n- 1: Completely accurate and comprehensive\n\nNow, I will evaluate each key in the output:\n\n1. **Categories**:\n   - The output correctly identifies the division as \"General,\" the department as \"Bottoms,\" and the class as \"Shorts.\" However, it introduces \"My Category\" as \"Romper,\" which is not part of the original classification and could be seen as unnecessary. \n   - **Rating**: 0.75 (mostly accurate but includes an unnecessary category)\n\n2. **Sentiment**:\n   - The sentiment is correctly identified as \"positive,\" which aligns with the enthusiastic tone of the review (\"adorable!!!\" and \"i love\").\n   - **Rating**: 1 (completely accurate)\n\n3. **Summary**:\n   - The summary accurately captures the essence of the review, noting that the customer finds the romper adorable and appreciates the long sleeves. It succinctly reflects the main points of the review.\n   - **Rating**: 1 (completely accurate and comprehensive)\n\n4. **Personalized Message**:\n   - The personalized message is appropriate and expresses gratitude for the feedback. It also reiterates the customer's positive feelings about the romper and its long sleeves, which is a nice touch.\n   - **Rating**: 1 (completely accurate and well-crafted)\n\n5. **Retail Insight**:\n   - The retail insight provides a relevant suggestion to highlight unique features in marketing materials, which is a logical conclusion based on the review's emphasis on the long sleeves. It connects well with the customer's positive sentiment.\n   - **Rating**: 1 (completely accurate and insightful)\n\nBased on the evaluations, here is the final output:\n\n```json\n{\n  \"categories\": 0.75,\n  \"sentiment\": 1,\n  \"summary\": 1,\n  \"personalized_message\": 1,\n  \"retail_insight\": 1\n}\n```"
}
{
  "judge_response1": "To evaluate the zero-shot model output, I will assess each key based on its accuracy and completeness in relation to the original review. The scale for rating will be as follows:\n\n- 0: Completely inaccurate or irrelevant\n- 0.25: Somewhat inaccurate or missing key elements\n- 0.5: Partially accurate but lacks detail or clarity\n- 0.75: Mostly accurate with minor issues\n- 1: Completely accurate and comprehensive\n\nNow, I will rate each part of the output:\n\n1. **Categories**:\n   - The categories provided (division, department, class, and My Category) accurately reflect the information in the original review. The addition of \"My Category\": \"Vests\" is appropriate given that the review is about a vest.\n   - **Rating**: 1.0\n\n2. **Sentiment**:\n   - The sentiment is correctly identified as \"positive,\" which aligns with the enthusiastic tone of the review. The reviewer expresses satisfaction with the fit, style, and versatility of the vest.\n   - **Rating**: 1.0\n\n3. **Summary**:\n   - The summary captures the main points of the review effectively, highlighting the customer's satisfaction with the fit, style, and versatility, as well as the fact that they ordered another color. It is concise and accurately reflects the review's content.\n   - **Rating**: 1.0\n\n4. **Personalized Message**:\n   - The personalized message is warm and appreciative, which is appropriate for customer feedback. It acknowledges the customer's positive experience and encourages them to enjoy the product. However, it could have included a mention of the versatility or timelessness of the vest, which were key points in the review.\n   - **Rating**: 0.75\n\n5. **Retail Insight**:\n   - The retail insight is relevant and actionable, suggesting that the retailer consider offering more color options and styles for vests. This aligns well with the reviewer's comments about versatility and timeless fashion. It effectively translates the customer's feedback into a business recommendation.\n   - **Rating**: 1.0\n\nBased on the evaluations, here is the final output:\n\n```json\n{\n  \"categories\": 1.0,\n  \"sentiment\": 1.0,\n  \"summary\": 1.0,\n  \"personalized_message\": 0.75,\n  \"retail_insight\": 1.0\n}"
}
{
  "judge_response1": "To evaluate the zero-shot model output, I will assess each key based on its accuracy and completeness in relation to the original review. The scale for rating will be as follows:\n\n- 0: Completely inaccurate or irrelevant\n- 0.25: Somewhat inaccurate or missing key elements\n- 0.5: Partially accurate but lacks depth or clarity\n- 0.75: Mostly accurate with minor issues\n- 1: Completely accurate and comprehensive\n\nNow, I will evaluate each part of the output:\n\n1. **Categories**:\n   - The categories provided (division, department, class, and an additional category \"My Category\") are accurate and relevant to the review. The inclusion of \"Fashion\" as a category is appropriate given the context of the review.\n   - **Rating**: 1.0\n\n2. **Sentiment**:\n   - The sentiment is correctly identified as \"positive,\" reflecting the reviewer's enthusiasm and satisfaction with the dress. The review conveys a clear positive experience, which the model captured accurately.\n   - **Rating**: 1.0\n\n3. **Summary**:\n   - The summary accurately captures the essence of the review, highlighting the initial hesitation due to price, the positive experience after trying on the dress, and the willingness to pay full price. It succinctly conveys the main points without omitting important details.\n   - **Rating**: 1.0\n\n4. **Personalized Message**:\n   - The personalized message is warm and engaging, thanking the reviewer for their experience and acknowledging their styling tips. It aligns well with the positive sentiment of the review and adds a personal touch.\n   - **Rating**: 1.0\n\n5. **Retail Insight**:\n   - The retail insight is practical and relevant, suggesting that offering more promotional sales could help convert hesitant customers. This aligns with the reviewer's experience of being initially deterred by price, making it a valuable suggestion for retailers.\n   - **Rating**: 1.0\n\nBased on the evaluations, here is the final output:\n\n```json\n{\n  \"categories\": 1.0,\n  \"sentiment\": 1.0,\n  \"summary\": 1.0,\n  \"personalized_message\": 1.0,\n  \"retail_insight\": 1.0\n}\n```"
}
{
  "judge_response1": "To evaluate the zero-shot model output, I will assess each key based on its accuracy and completeness in relation to the original review. The scale for rating each key is as follows:\n\n- 0: Completely inaccurate or irrelevant\n- 0.25: Somewhat inaccurate or missing key elements\n- 0.5: Partially accurate but lacks detail or clarity\n- 0.75: Mostly accurate with minor issues\n- 1: Completely accurate and comprehensive\n\nNow, I will rate each part of the output:\n\n1. **Categories**:\n   - The categories provided (division, department, class, and My Category) are accurate and relevant to the review. The addition of \"My Category\" as \"Apparel\" is appropriate, as it encompasses the other categories.\n   - **Rating**: 1.0\n\n2. **Sentiment**:\n   - The sentiment is labeled as \"neutral,\" which is somewhat misleading. The review contains mixed feelings: the reviewer mentions the dress runs large and has a scratchy material, but also appreciates the color and the stylist's opinion. A more accurate sentiment might be \"mixed\" or \"neutral-positive.\"\n   - **Rating**: 0.5\n\n3. **Summary**:\n   - The summary captures the main points of the review, including the sizing issue, material discomfort, and positive remarks about the color and stylist's opinion. However, it could mention the zipper issue, which is a notable part of the review.\n   - **Rating**: 0.75\n\n4. **Personalized Message**:\n   - The personalized message is appropriate and acknowledges the feedback effectively. It expresses appreciation for the insights and addresses the reviewer's considerations about keeping the dress. However, it could be slightly more tailored by mentioning the zipper issue.\n   - **Rating**: 0.75\n\n5. **Retail Insight**:\n   - The retail insight is practical and relevant, suggesting improvements in sizing charts, fabric descriptions, and addressing zipper functionality. This aligns well with the review's content and provides actionable advice.\n   - **Rating**: 1.0\n\nBased on the evaluations, here is the final output:\n\n```json\n{\n  \"categories\": 1.0,\n  \"sentiment\": 0.5,\n  \"summary\": 0.75,\n  \"personalized_message\": 0.75,\n  \"retail_insight\": 1.0\n}\n```"
}
{
  "judge_response1": "To evaluate the zero-shot model output, I will assess each key based on its accuracy and completeness in relation to the original review provided in the prompt. The scale for rating will be as follows:\n\n- 0: Completely inaccurate or irrelevant\n- 0.25: Somewhat inaccurate or missing key elements\n- 0.5: Partially accurate but lacks detail or clarity\n- 0.75: Mostly accurate with minor issues\n- 1: Completely accurate and comprehensive\n\nNow, I will evaluate each key:\n\n1. **Categories**:\n   - The categories provided (division, department, class, and an additional category \"My Category\") are accurate and align with the original review. The division, department, and class match the context of the review, which discusses a top (blouse) in the General Petite category.\n   - **Rating**: 1.0\n\n2. **Sentiment**:\n   - The sentiment is correctly identified as \"positive,\" which reflects the overall tone of the review. The reviewer expresses happiness and satisfaction with the product, which aligns with the sentiment classification.\n   - **Rating**: 1.0\n\n3. **Summary**:\n   - The summary accurately captures the essence of the review, mentioning the reviewer's satisfaction, the flattering fit, perfect length, and beautiful colors. It succinctly conveys the main points without omitting any critical details.\n   - **Rating**: 1.0\n\n4. **Personalized Message**:\n   - The personalized message is warm and appreciative, reflecting the positive sentiment of the review. It acknowledges the reviewer's feedback and expresses gratitude, which is appropriate for a customer interaction context.\n   - **Rating**: 1.0\n\n5. **Retail Insight**:\n   - The retail insight provides a thoughtful suggestion about offering more detailed sizing guides and customer photos, which is relevant given the reviewer's mention of sizing and fit. This insight is practical and aligns well with the context of the review.\n   - **Rating**: 1.0\n\nBased on the evaluations, here is the final output:\n\n```json\n{\n  \"categories\": 1.0,\n  \"sentiment\": 1.0,\n  \"summary\": 1.0,\n  \"personalized_message\": 1.0,\n  \"retail_insight\": 1.0\n}\n```"
}
{
  "judge_response1": "To evaluate the zero-shot model output, I will assess each key based on its accuracy and relevance to the original review. The scale for rating each key is as follows:\n\n- 0: Completely inaccurate or irrelevant\n- 0.25: Somewhat inaccurate or partially relevant\n- 0.5: Moderately accurate but lacking in detail or clarity\n- 0.75: Mostly accurate with minor issues\n- 1: Completely accurate and relevant\n\nNow, I will rate each part of the output:\n\n1. **Categories**:\n   - The categories provided (division, department, class, and My Category) are accurate and relevant to the context of the review. The addition of \"My Category\" as \"Apparel\" is appropriate given the context of the cardigan.\n   - **Rating**: 1.0\n\n2. **Sentiment**:\n   - The sentiment labeled as \"neutral\" does not accurately reflect the reviewer's feelings. The reviewer expresses disappointment and sadness about the fit of the cardigan, which indicates a negative sentiment rather than neutral.\n   - **Rating**: 0.25\n\n3. **Summary**:\n   - The summary captures the main points of the review, including the disappointment about the fit and the positive remarks about the cardigan's design and quality. However, it could be more concise and directly mention the emotional aspect of the review.\n   - **Rating**: 0.75\n\n4. **Personalized Message**:\n   - The personalized message is appropriate and empathetic, acknowledging the reviewer's feedback and expressing regret that the item did not fit. It also appreciates the positive comments about the design and quality, which is a good touch.\n   - **Rating**: 1.0\n\n5. **Retail Insight**:\n   - The retail insight is practical and relevant, suggesting improvements in sizing information to help customers make better decisions. This aligns well with the review's focus on fit issues.\n   - **Rating**: 1.0\n\nBased on the evaluations, here is the final output:\n\n```json\n{\n  \"categories\": 1.0,\n  \"sentiment\": 0.25,\n  \"summary\": 0.75,\n  \"personalized_message\": 1.0,\n  \"retail_insight\": 1.0\n}\n```"
}
{
  "judge_response1": "To evaluate the zero-shot model output, I will assess each key based on its accuracy and relevance to the original review. The scale for rating is as follows:\n\n- 0: Completely inaccurate or irrelevant\n- 0.25: Somewhat inaccurate or partially relevant\n- 0.5: Moderately accurate but lacks detail or clarity\n- 0.75: Mostly accurate with minor issues\n- 1: Completely accurate and highly relevant\n\nNow, I will rate each part of the output:\n\n1. **Categories**:\n   - The categories provided (division, department, class, My Category) are accurate and relevant to the original review. The addition of \"My Category\" as \"Apparel\" is appropriate, as it encompasses the other categories.\n   - **Rating**: 1.0\n\n2. **Sentiment**:\n   - The sentiment is correctly identified as \"negative,\" reflecting the reviewer's dissatisfaction with the fit of the dress, particularly for larger chests. The review expresses frustration about the dress's fit, which aligns with a negative sentiment.\n   - **Rating**: 1.0\n\n3. **Summary**:\n   - The summary captures the essence of the review well, noting the high quality and design of the dress while highlighting the fit issues for larger chests. It also mentions the dress's unsuitability for work, which is a key point in the review.\n   - **Rating**: 1.0\n\n4. **Personalized Message**:\n   - The personalized message is thoughtful and acknowledges the feedback provided by the reviewer. It expresses appreciation for the insights on fit, which is relevant to the review. However, it could be slightly more personalized by addressing the specific issue of the low cut and breathability.\n   - **Rating**: 0.75\n\n5. **Retail Insight**:\n   - The retail insight is practical and directly addresses the concerns raised in the review. It suggests offering a wider range of sizes and styles for different body types, which is a relevant recommendation based on the reviewer's experience. The mention of providing detailed fit guides is also a valuable suggestion.\n   - **Rating**: 1.0\n\nBased on the evaluations, here is the final output:\n\n```json\n{\n  \"categories\": 1.0,\n  \"sentiment\": 1.0,\n  \"summary\": 1.0,\n  \"personalized_message\": 0.75,"
}
{
  "judge_response1": "To evaluate the zero-shot model output, I will assess each key based on its accuracy and completeness in relation to the original review provided in the prompt. The scale for rating each key is as follows:\n\n- 0: Completely inaccurate or missing\n- 0.25: Somewhat inaccurate or incomplete\n- 0.5: Moderately accurate but with notable issues\n- 0.75: Mostly accurate with minor issues\n- 1: Completely accurate and comprehensive\n\nNow, I will evaluate each part of the output:\n\n1. **Categories**:\n   - The categories provided (division, department, class) match the original prompt accurately. The addition of \"My Category\" as \"Petite Maxi Dresses\" is appropriate given the review context.\n   - **Rating**: 1.0\n\n2. **Sentiment**:\n   - The sentiment is correctly identified as \"positive,\" which aligns with the enthusiastic tone of the review.\n   - **Rating**: 1.0\n\n3. **Summary**:\n   - The summary accurately captures the key points of the review, including the fit, versatility, softness, and travel suitability of the dress. It reflects the reviewer's positive experience well.\n   - **Rating**: 1.0\n\n4. **Personalized Message**:\n   - The personalized message is warm and acknowledges the reviewer's feedback effectively. It also encourages the customer to enjoy their trip, which is relevant to the review context.\n   - **Rating**: 1.0\n\n5. **Retail Insight**:\n   - The retail insight is thoughtful and relevant, suggesting an expansion of petite sizes and promoting travel-friendly features. This aligns well with the review's emphasis on the dress's travel suitability.\n   - **Rating**: 1.0\n\nBased on the evaluations, here is the final output:\n\n```json\n{\n  \"categories\": 1.0,\n  \"sentiment\": 1.0,\n  \"summary\": 1.0,\n  \"personalized_message\": 1.0,\n  \"retail_insight\": 1.0\n}\n```"
}
{
  "judge_response1": "To evaluate the zero-shot model output, I will assess each key based on its accuracy and completeness in relation to the original review provided. The scale for rating each key is as follows:\n\n- 0: Completely inaccurate or missing\n- 0.25: Somewhat inaccurate or incomplete\n- 0.5: Moderately accurate but with notable issues\n- 0.75: Mostly accurate with minor issues\n- 1: Completely accurate and comprehensive\n\nNow, I will rate each part of the output:\n\n1. **Categories**:\n   - The categories provided (division, department, class) accurately reflect the original review. The addition of \"My Category\": \"Fall Fashion\" is relevant and aligns with the seasonal context of the review. \n   - **Rating**: 1.0\n\n2. **Sentiment**:\n   - The sentiment is correctly identified as \"positive,\" which aligns with the overall tone of the review. The reviewer expresses satisfaction with the dress and mentions receiving compliments.\n   - **Rating**: 1.0\n\n3. **Summary**:\n   - The summary captures the main points of the review accurately, including the positive aspects (perfect for fall, appreciation of the velvet material, receiving compliments) and the desire for a more fitted bottom half. It is concise and covers the essential elements.\n   - **Rating**: 1.0\n\n4. **Personalized Message**:\n   - The personalized message is appropriate and acknowledges the customer's feedback positively. It thanks the customer for their input and addresses the suggestion regarding the fit, which shows attentiveness to customer feedback.\n   - **Rating**: 1.0\n\n5. **Retail Insight**:\n   - The retail insight is practical and relevant, suggesting that the retailer consider offering a variety of fits for the same dress style. This aligns well with the customer's feedback about the fit and could enhance customer satisfaction.\n   - **Rating**: 1.0\n\nBased on the evaluations, here is the final output:\n\n```json\n{\n  \"categories\": 1.0,\n  \"sentiment\": 1.0,\n  \"summary\": 1.0,\n  \"personalized_message\": 1.0,\n  \"retail_insight\": 1.0\n}\n```"
}
{
  "judge_response1": "To evaluate the zero-shot model output, I will assess each key based on its accuracy and relevance to the original review. The scale for rating will be as follows:\n\n- 0: Completely inaccurate or irrelevant\n- 0.1 - 0.4: Somewhat inaccurate or lacking in relevance\n- 0.5: Neutral or average accuracy\n- 0.6 - 0.9: Mostly accurate and relevant, with minor issues\n- 1: Completely accurate and highly relevant\n\nNow, I will evaluate each part of the output:\n\n1. **Categories**:\n   - The categories provided (division, department, class, and My Category) are accurate and relevant to the original review. The inclusion of \"Apparel\" as a category is appropriate as it encompasses dresses.\n   - **Rating**: 1.0\n\n2. **Sentiment**:\n   - The sentiment is correctly identified as \"negative,\" which aligns with the reviewer's dissatisfaction with the dress.\n   - **Rating**: 1.0\n\n3. **Summary**:\n   - The summary accurately captures the main points of the review: dissatisfaction with fit, fabric quality, and color discrepancy. It succinctly reflects the review's content.\n   - **Rating**: 1.0\n\n4. **Personalized Message**:\n   - The personalized message is polite and acknowledges the customer's feedback. It addresses the issues raised in the review, such as fit and color, and expresses appreciation for the input. However, it could be more specific by mentioning the issues of fabric quality and fit explicitly.\n   - **Rating**: 0.8\n\n5. **Retail Insight**:\n   - The retail insight provides practical suggestions for improvement, such as enhancing product descriptions and offering a size guide. These suggestions are relevant to the issues raised in the review and could help prevent similar complaints in the future.\n   - **Rating**: 1.0\n\nNow, I will compile the evaluation into a dictionary format:\n\n```json\n{\n  \"categories\": 1.0,\n  \"sentiment\": 1.0,\n  \"summary\": 1.0,\n  \"personalized_message\": 0.8,\n  \"retail_insight\": 1.0\n}\n```"
}
{
  "judge_response1": "To evaluate the zero-shot model output, I will assess each key based on its accuracy and completeness in relation to the original review. The scale for rating will be as follows:\n\n- 0: Completely inaccurate or irrelevant\n- 0.25: Somewhat inaccurate or lacking detail\n- 0.5: Partially accurate but missing key elements\n- 0.75: Mostly accurate with minor issues\n- 1: Completely accurate and comprehensive\n\nNow, I will rate each part of the output:\n\n1. **Categories**:\n   - The categories provided (division, department, class, and an additional category \"My Category\") are accurate and relevant to the review. The inclusion of \"Fashion\" as a category is appropriate given the context of the review.\n   - **Rating**: 1.0\n\n2. **Sentiment**:\n   - The sentiment is correctly identified as \"positive,\" reflecting the overall tone of the review, where the reviewer expresses appreciation for the dress and the designer.\n   - **Rating**: 1.0\n\n3. **Summary**:\n   - The summary captures the essence of the review well, highlighting the reviewer's appreciation for the dress and mentioning the fit issues. However, it could have included more detail about the specific aspects of the dress that were appreciated (e.g., the lace design).\n   - **Rating**: 0.75\n\n4. **Personalized Message**:\n   - The personalized message is warm and engaging, effectively acknowledging the reviewer's positive experience. It aligns well with the sentiment expressed in the review.\n   - **Rating**: 1.0\n\n5. **Retail Insight**:\n   - The retail insight is practical and relevant, suggesting a way to improve customer satisfaction by addressing fit issues. This aligns with the reviewer's comments about the fit of the dress. However, it could have been more specific about the types of adjustments that could be made.\n   - **Rating**: 0.75\n\nNow, I will compile the evaluation into a dictionary format:\n\n```json\n{\n  \"categories\": 1.0,\n  \"sentiment\": 1.0,\n  \"summary\": 0.75,\n  \"personalized_message\": 1.0,\n  \"retail_insight\": 0.75\n}\n```"
}
{
  "judge_response1": "To evaluate the zero-shot model output, I will assess each key based on its accuracy and completeness in relation to the original review provided. The scale for rating will be as follows:\n\n- 0: Completely inaccurate or irrelevant\n- 0.25: Somewhat inaccurate or missing key elements\n- 0.5: Partially accurate but lacks depth or clarity\n- 0.75: Mostly accurate with minor issues\n- 1: Completely accurate and comprehensive\n\nNow, I will evaluate each part of the output:\n\n1. **Categories**:\n   - The categories provided (division, department, class, and My Category) accurately reflect the information in the original prompt. The addition of \"My Category\" as \"Apparel\" is a reasonable generalization.\n   - **Rating**: 1.0\n\n2. **Sentiment**:\n   - The sentiment is correctly identified as \"positive,\" as the review expresses satisfaction with the dress being comfortable and cute, despite a minor issue with the torso length.\n   - **Rating**: 1.0\n\n3. **Summary**:\n   - The summary captures the essence of the review well, noting both the positive aspects (comfort and cuteness) and the potential issue (torso length). It is concise and accurately reflects the review's content.\n   - **Rating**: 1.0\n\n4. **Personalized Message**:\n   - The personalized message is appropriate and acknowledges the customer's feedback positively. It also addresses the concern about the torso length, indicating that the company values customer input. However, it could be slightly more personalized by mentioning the specific dress.\n   - **Rating**: 0.75\n\n5. **Retail Insight**:\n   - The retail insight provides a constructive suggestion based on the review, which is relevant and actionable. It addresses the concern about torso length and suggests a solution that could enhance customer satisfaction. This insight is well-aligned with the review's content.\n   - **Rating**: 1.0\n\nNow, compiling the evaluations into a dictionary format:\n\n```json\n{\n  \"categories\": 1.0,\n  \"sentiment\": 1.0,\n  \"summary\": 1.0,\n  \"personalized_message\": 0.75,\n  \"retail_insight\": 1.0\n}\n```"
}
{
  "judge_response1": "To evaluate the zero-shot model output, I will assess each key based on its accuracy and completeness in relation to the original review. The scale for rating each key is as follows:\n\n- 0: Completely inaccurate or missing\n- 0.25: Somewhat inaccurate or incomplete\n- 0.5: Moderately accurate but with notable issues\n- 0.75: Mostly accurate with minor issues\n- 1: Completely accurate and comprehensive\n\nNow, I will evaluate each key:\n\n1. **Categories**:\n   - The categories provided (division, department, class, and My Category) accurately reflect the information in the original review. The addition of \"My Category\" as \"Apparel\" is appropriate and relevant.\n   - **Rating**: 1.0\n\n2. **Sentiment**:\n   - The sentiment is correctly identified as \"negative,\" which aligns with the reviewer's overall disappointment with the product.\n   - **Rating**: 1.0\n\n3. **Summary**:\n   - The summary captures the main points of the review: the customer's love for the design, disappointment with color brightness, and the itchy material leading to a return. It is concise and accurately reflects the review's content.\n   - **Rating**: 1.0\n\n4. **Personalized Message**:\n   - The personalized message is empathetic and acknowledges the customer's feedback. It expresses appreciation for the input and addresses the specific issues raised (design, colors, and material). However, it could be slightly more personalized by mentioning the specific issues (itchiness and color brightness) directly.\n   - **Rating**: 0.75\n\n5. **Retail Insight**:\n   - The retail insight provides actionable suggestions based on the review, such as improving color representation and exploring softer materials. This is relevant and useful for enhancing customer satisfaction.\n   - **Rating**: 1.0\n\nBased on the evaluations, here is the final output:\n\n```json\n{\n  \"categories\": 1.0,\n  \"sentiment\": 1.0,\n  \"summary\": 1.0,\n  \"personalized_message\": 0.75,\n  \"retail_insight\": 1.0\n}\n```"
}
{
  "judge_response1": "To evaluate the zero-shot model output, I will assess each key based on its accuracy and relevance to the original review. The scale for rating is as follows:\n\n- 0: Completely inaccurate or irrelevant\n- 0.25: Somewhat inaccurate or lacks relevance\n- 0.5: Moderately accurate but could be improved\n- 0.75: Mostly accurate with minor issues\n- 1: Completely accurate and relevant\n\nNow, I will rate each part of the output:\n\n1. **Categories**:\n   - The categories provided (division, department, class, and My Category) are accurate and align with the original review. The addition of \"Fit Issues\" is appropriate given the context of the review.\n   - **Rating**: 1.0\n\n2. **Sentiment**:\n   - The sentiment is correctly identified as \"negative,\" which reflects the reviewer's dissatisfaction with the fit of the shirt.\n   - **Rating**: 1.0\n\n3. **Summary**:\n   - The summary accurately captures the main points of the review, specifically the mismatch between the expected fit and the actual fit described by the reviewer. It mentions the shirt being \"short and wide\" and compares it to a \"trapezoid shape,\" which is a direct reflection of the review.\n   - **Rating**: 1.0\n\n4. **Personalized Message**:\n   - The personalized message is empathetic and acknowledges the reviewer's feedback. It expresses regret for the unsatisfactory experience and indicates a commitment to improving product descriptions. However, it could be slightly more personalized by mentioning the specific issue of fit.\n   - **Rating**: 0.75\n\n5. **Retail Insight**:\n   - The retail insight is practical and relevant, suggesting improvements in size guides, fit descriptions, and customer photos. This aligns well with the review's focus on fit issues and could help prevent similar complaints in the future.\n   - **Rating**: 1.0\n\nBased on the evaluations, here is the final output:\n\n```json\n{\n  \"categories\": 1.0,\n  \"sentiment\": 1.0,\n  \"summary\": 1.0,\n  \"personalized_message\": 0.75,\n  \"retail_insight\": 1.0\n}\n```"
}
{
  "judge_response1": "To evaluate the zero-shot model output, I will assess each key based on its accuracy and relevance to the original review. The scale for rating is as follows:\n\n- 0: Completely inaccurate or irrelevant\n- 0.25: Somewhat inaccurate or partially relevant\n- 0.5: Moderately accurate or relevant, but with significant issues\n- 0.75: Mostly accurate or relevant, with minor issues\n- 1: Completely accurate and relevant\n\nNow, I will rate each part of the output:\n\n1. **Categories**:\n   - The categories provided (division, department, class) match the original prompt accurately. The addition of \"My Category\": \"Fashion\" is a reasonable extrapolation, as blouses fall under fashion. \n   - **Rating**: 1.0\n\n2. **Sentiment**:\n   - The sentiment \"positive\" accurately reflects the review, which expresses a favorable opinion about the blouse.\n   - **Rating**: 1.0\n\n3. **Summary**:\n   - The summary captures the essence of the review well, stating that the reviewer finds the blouse beautiful and flattering. It is concise and directly related to the review content.\n   - **Rating**: 1.0\n\n4. **Personalized Message**:\n   - The personalized message is appropriate and acknowledges the positive feedback. It expresses gratitude and aligns well with the sentiment of the review.\n   - **Rating**: 1.0\n\n5. **Retail Insight**:\n   - The retail insight suggests highlighting the flattering features of blouses in marketing, which is a logical recommendation based on the review's positive sentiment. It is relevant and actionable.\n   - **Rating**: 1.0\n\nBased on the evaluations, here is the final output:\n\n```json\n{\n  \"categories\": 1.0,\n  \"sentiment\": 1.0,\n  \"summary\": 1.0,\n  \"personalized_message\": 1.0,\n  \"retail_insight\": 1.0\n}\n```"
}
{
  "judge_response1": "To evaluate the zero-shot model output, I will assess each key based on its accuracy and completeness in relation to the original review provided in the prompt. The scale for rating will be as follows:\n\n- 0: Completely inaccurate or irrelevant\n- 0.1 - 0.4: Somewhat accurate but missing key elements or contains significant errors\n- 0.5 - 0.7: Mostly accurate with minor issues or omissions\n- 0.8 - 1: Highly accurate and complete, capturing all essential elements\n\nNow, I will evaluate each key in the output:\n\n1. **Categories**:\n   - The categories provided (division, department, class, and My Category) are accurate and align with the original prompt. The addition of \"My Category\" as \"Cardigans\" is appropriate given the context of the review.\n   - **Rating**: 1.0\n\n2. **Sentiment**:\n   - The sentiment is correctly identified as \"positive,\" reflecting the reviewer's overall appreciation for the cardigan despite mentioning a drawback.\n   - **Rating**: 1.0\n\n3. **Summary**:\n   - The summary accurately captures the main points of the review: the reviewer's appreciation for the cardigan's versatility and beauty, as well as the limitation posed by the short sleeves. It succinctly conveys the essence of the review.\n   - **Rating**: 1.0\n\n4. **Personalized Message**:\n   - The personalized message is appropriate and acknowledges the reviewer's feedback positively. It addresses the concern about short sleeves, which shows attentiveness to the reviewer's experience.\n   - **Rating**: 1.0\n\n5. **Retail Insight**:\n   - The retail insight is relevant and constructive, suggesting a potential improvement (offering more options in sleeve lengths) that aligns with the reviewer's concern. This insight could help the retailer enhance customer satisfaction.\n   - **Rating**: 1.0\n\nBased on the evaluations, here is the final output:\n\n```json\n{\n  \"categories\": 1.0,\n  \"sentiment\": 1.0,\n  \"summary\": 1.0,\n  \"personalized_message\": 1.0,\n  \"retail_insight\": 1.0\n}\n```"
}
{
  "judge_response1": "To evaluate the zero-shot model output, I will assess each key based on its accuracy and completeness in relation to the original review provided. The scale for rating will be as follows:\n\n- 0: Completely inaccurate or irrelevant\n- 0.25: Somewhat inaccurate or missing key elements\n- 0.5: Partially accurate but lacks detail or clarity\n- 0.75: Mostly accurate with minor issues\n- 1: Completely accurate and comprehensive\n\nNow, I will evaluate each key:\n\n1. **Categories**:\n   - The categories provided (division, department, class, and My Category) accurately reflect the information in the original review. The addition of \"Lace Detail Tops\" is a reasonable categorization based on the review's mention of lace detail.\n   - **Rating**: 1.0\n\n2. **Sentiment**:\n   - The sentiment is correctly identified as \"positive,\" which aligns with the review's praise for the lace detail and softness of the material.\n   - **Rating**: 1.0\n\n3. **Summary**:\n   - The summary accurately captures the main points of the review: the beautiful lace detail, the softness of the material, and the note about sizing. It is concise and informative.\n   - **Rating**: 1.0\n\n4. **Personalized Message**:\n   - The personalized message is appropriate and acknowledges the positive aspects of the review while also addressing the sizing concern. It conveys appreciation for the feedback, which is a good customer service practice.\n   - **Rating**: 1.0\n\n5. **Retail Insight**:\n   - The retail insight suggests offering a size guide or recommendations for sizing adjustments, which is a practical suggestion based on the review's mention of sizing. This insight is relevant and actionable.\n   - **Rating**: 1.0\n\nOverall, the zero-shot model output is accurate and comprehensive across all evaluated keys. \n\nFinal Evaluation:\n```json\n{\n  \"categories\": 1.0,\n  \"sentiment\": 1.0,\n  \"summary\": 1.0,\n  \"personalized_message\": 1.0,\n  \"retail_insight\": 1.0\n}\n```"
}
{
  "judge_response1": "To evaluate the zero-shot model output, I will assess each key based on its accuracy and completeness in relation to the original review provided. The scale for rating will be as follows:\n\n- 0: Completely inaccurate or irrelevant\n- 0.25: Somewhat inaccurate or missing key elements\n- 0.5: Partially accurate but lacks detail or clarity\n- 0.75: Mostly accurate with minor issues\n- 1: Completely accurate and comprehensive\n\nNow, I will evaluate each part of the output:\n\n1. **Categories**:\n   - The categories provided (division, department, class) accurately reflect the original prompt. The addition of \"My Category\" as \"Casual Wear\" is appropriate given the review mentions the dress's versatility for work and casual occasions.\n   - **Rating**: 1.0\n\n2. **Sentiment**:\n   - The sentiment is correctly identified as \"positive,\" which aligns with the enthusiastic tone of the review.\n   - **Rating**: 1.0\n\n3. **Summary**:\n   - The summary captures the essence of the review, noting the customer's satisfaction and the dress's versatility and quality. It is concise and accurately reflects the review's content.\n   - **Rating**: 1.0\n\n4. **Personalized Message**:\n   - The personalized message is warm and appreciative, which is suitable for customer feedback. It acknowledges the customer's positive experience and reinforces the connection with the brand.\n   - **Rating**: 1.0\n\n5. **Retail Insight**:\n   - The retail insight suggests highlighting the versatility of dresses in marketing, which is a logical recommendation based on the review's emphasis on the dress's suitability for multiple occasions. This insight is actionable and relevant.\n   - **Rating**: 1.0\n\nBased on the evaluations, here is the final output:\n\n```json\n{\n  \"categories\": 1.0,\n  \"sentiment\": 1.0,\n  \"summary\": 1.0,\n  \"personalized_message\": 1.0,\n  \"retail_insight\": 1.0\n}\n```"
}
{
  "judge_response1": "To evaluate the zero-shot model output, I will assess each key based on its accuracy and relevance to the original review. The scale for rating is as follows:\n\n- 0: Completely inaccurate or irrelevant\n- 0.25: Somewhat inaccurate or partially relevant\n- 0.5: Neutral; neither accurate nor inaccurate\n- 0.75: Mostly accurate with minor issues\n- 1: Completely accurate and relevant\n\nNow, I will rate each part of the output:\n\n1. **Categories**:\n   - The categories provided (division, department, class, and an additional category \"My Category\") are accurate and align with the original review. The classification of the skirt under \"Bottoms\" and \"Skirts\" is correct.\n   - **Rating**: 1.0\n\n2. **Sentiment**:\n   - The sentiment is labeled as \"neutral,\" which is appropriate given that the reviewer expresses both positive and negative feelings about the skirt. They appreciate the length and pattern but find it unflattering, which balances out to a neutral sentiment.\n   - **Rating**: 1.0\n\n3. **Summary**:\n   - The summary accurately captures the essence of the review, highlighting both the positive aspects (midi length and pattern) and the negative aspect (unflattering fit). It succinctly reflects the reviewer's experience.\n   - **Rating**: 1.0\n\n4. **Personalized Message**:\n   - The personalized message is warm and acknowledges the reviewer's feedback. It reinforces the positive aspects of the skirt while also recognizing the subjective nature of fashion. This is a thoughtful response that aligns well with the review.\n   - **Rating**: 1.0\n\n5. **Retail Insight**:\n   - The retail insight suggests offering a wider range of sizes and styles, which is a relevant recommendation based on the reviewer's experience of the skirt not being flattering. It also suggests providing styling tips, which could help customers better visualize the product. This insight is practical and directly related to the review.\n   - **Rating**: 1.0\n\nOverall, the zero-shot model output is accurate and relevant across all categories. Here is the final evaluation in dictionary format:\n\n```json\n{\n  \"categories\": 1.0,\n  \"sentiment\": 1.0,\n  \"summary\": 1.0,\n  \"personalized_message\": 1.0,\n  \"retail"
}
{
  "judge_response1": "To evaluate the zero-shot model output, I will assess each key based on its accuracy and relevance to the original review. The scale for rating each key is as follows:\n\n- 0: Completely inaccurate or irrelevant\n- 0.25: Somewhat inaccurate or lacks relevance\n- 0.5: Moderately accurate but could be improved\n- 0.75: Mostly accurate with minor issues\n- 1: Completely accurate and relevant\n\nNow, I will rate each part of the output:\n\n1. **Categories**:\n   - The categories provided (division, department, class, and My Category) accurately reflect the information in the original review. The addition of \"My Category\" as \"Apparel\" is appropriate and relevant.\n   - **Rating**: 1.0\n\n2. **Sentiment**:\n   - The sentiment is correctly identified as \"negative,\" which aligns with the reviewer's disappointment and intention to return the skirt.\n   - **Rating**: 1.0\n\n3. **Summary**:\n   - The summary accurately captures the main points of the review, including the disappointment with quality and the lack of lining, as well as the price concern. It succinctly conveys the essence of the review.\n   - **Rating**: 1.0\n\n4. **Personalized Message**:\n   - The personalized message is appropriate and empathetic, acknowledging the customer's feedback and expressing regret that the product did not meet expectations. It also indicates that the feedback will be considered for future improvements, which is a good customer service approach.\n   - **Rating**: 1.0\n\n5. **Retail Insight**:\n   - The retail insight provides a constructive suggestion to enhance the quality of materials and add lining to skirts, which directly addresses the customer's concerns. This insight is relevant and actionable, making it a valuable addition.\n   - **Rating**: 1.0\n\nBased on the evaluations, here is the final output:\n\n```json\n{\n  \"categories\": 1.0,\n  \"sentiment\": 1.0,\n  \"summary\": 1.0,\n  \"personalized_message\": 1.0,\n  \"retail_insight\": 1.0\n}\n```"
}
{
  "judge_response1": "To evaluate the zero-shot model output, I will assess each key based on its accuracy and relevance to the original review. The scale for rating will be as follows:\n\n- 0: Completely inaccurate or irrelevant\n- 0.1 - 0.4: Somewhat inaccurate or lacking in relevance\n- 0.5: Neutral or average accuracy\n- 0.6 - 0.9: Mostly accurate and relevant\n- 1: Completely accurate and highly relevant\n\nNow, I will evaluate each part of the output:\n\n1. **Categories**:\n   - The categories provided (division, department, class) match the original prompt accurately. The additional \"My Category\" is redundant but does not detract from the accuracy.\n   - **Rating**: 1.0\n\n2. **Sentiment**:\n   - The sentiment is labeled as \"negative,\" which aligns with the reviewer's feelings about the dress adding unwanted volume and their uncertainty about who it would look good on.\n   - **Rating**: 1.0\n\n3. **Summary**:\n   - The summary accurately captures the essence of the review, highlighting the reviewer's concern about the dress adding volume and their uncertainty regarding its suitability for others.\n   - **Rating**: 1.0\n\n4. **Personalized Message**:\n   - The personalized message is polite and acknowledges the reviewer's feedback. It encourages exploration of other styles, which is a good customer service approach. However, it could be more tailored to the specific concerns raised in the review about fit and volume.\n   - **Rating**: 0.8\n\n5. **Retail Insight**:\n   - The retail insight suggests providing detailed size guides and styling tips, which is relevant and actionable based on the reviewer's feedback about fit. It addresses a potential gap in customer support that could help others in similar situations.\n   - **Rating**: 1.0\n\nBased on this evaluation, the final output is as follows:\n\n```json\n{\n  \"categories\": 1.0,\n  \"sentiment\": 1.0,\n  \"summary\": 1.0,\n  \"personalized_message\": 0.8,\n  \"retail_insight\": 1.0\n}\n```"
}
{
  "judge_response1": "To evaluate the zero-shot model output, I will assess each key based on its accuracy and completeness in relation to the original review. The scale for rating will be as follows:\n\n- 0: Completely inaccurate or irrelevant\n- 0.25: Somewhat inaccurate or missing key elements\n- 0.5: Partially accurate but lacks depth or clarity\n- 0.75: Mostly accurate with minor issues\n- 1: Completely accurate and comprehensive\n\nNow, I will rate each part of the output:\n\n1. **Categories**:\n   - The categories provided (division, department, class, and an additional category \"My Category\") are accurate and relevant to the review. The inclusion of \"Fashion\" as a category is appropriate given the context of the review.\n   - **Rating**: 1.0\n\n2. **Sentiment**:\n   - The sentiment is correctly identified as \"positive,\" which aligns with the enthusiastic tone of the review. The reviewer expresses strong satisfaction, which is captured well.\n   - **Rating**: 1.0\n\n3. **Summary**:\n   - The summary accurately reflects the reviewer's satisfaction with the dress, mentioning its quality, fit, and unique design. It captures the essence of the review without omitting any significant details.\n   - **Rating**: 1.0\n\n4. **Personalized Message**:\n   - The personalized message is warm and appreciative, which is appropriate given the positive nature of the review. It acknowledges the reviewer's satisfaction and reinforces the retailer's commitment to customer satisfaction.\n   - **Rating**: 1.0\n\n5. **Retail Insight**:\n   - The retail insight is practical and relevant, suggesting that the retailer should promote the unique features of the dress. This aligns well with the review's emphasis on quality and fit, making it a valuable suggestion for marketing.\n   - **Rating**: 1.0\n\nOverall, the zero-shot model output is accurate and comprehensive across all evaluated categories. Here is the final evaluation in dictionary format:\n\n```json\n{\n  \"categories\": 1.0,\n  \"sentiment\": 1.0,\n  \"summary\": 1.0,\n  \"personalized_message\": 1.0,\n  \"retail_insight\": 1.0\n}\n```"
}
{
  "judge_response1": "To evaluate the zero-shot model output, I will assess each key based on its accuracy and completeness in relation to the original review provided. The scale for rating will be as follows:\n\n- 0: Completely inaccurate or irrelevant\n- 0.1 - 0.4: Somewhat accurate but lacks important details or has significant errors\n- 0.5: Moderately accurate; captures some key points but misses others\n- 0.6 - 0.9: Mostly accurate; minor details may be missing or slightly off\n- 1: Completely accurate and comprehensive\n\nNow, I will evaluate each key:\n\n1. **Categories**:\n   - The categories provided in the output match the original review's context. The division, department, and class are correctly identified as \"General Petite,\" \"Dresses,\" and \"Dresses,\" respectively. The additional \"My Category\" is also appropriate.\n   - **Rating**: 1.0\n\n2. **Sentiment**:\n   - The sentiment is accurately identified as \"positive,\" which aligns with the customer's overall satisfaction expressed in the review.\n   - **Rating**: 1.0\n\n3. **Summary**:\n   - The summary effectively captures the main points of the review, including the customer's initial concerns and their satisfaction with the fit and style of the dress. It is concise and accurately reflects the review's content.\n   - **Rating**: 1.0\n\n4. **Personalized Message**:\n   - The personalized message is warm and acknowledges the customer's feedback, expressing enthusiasm about their satisfaction with the dress. It encourages future purchases, which is appropriate for a retail context.\n   - **Rating**: 1.0\n\n5. **Retail Insight**:\n   - The retail insight provides a valuable suggestion about highlighting customer reviews that emphasize fit and style, which is relevant to the concerns raised in the review. It also suggests showcasing diverse body types, which is a thoughtful addition.\n   - **Rating**: 1.0\n\nBased on the evaluations, here is the final output:\n\n```json\n{\n  \"categories\": 1.0,\n  \"sentiment\": 1.0,\n  \"summary\": 1.0,\n  \"personalized_message\": 1.0,\n  \"retail_insight\": 1.0\n}\n```"
}
{
  "judge_response1": "To evaluate the zero-shot model output, I will assess each key based on its accuracy and completeness in relation to the original review provided in the prompt. The scale for evaluation is as follows:\n\n- 0: Completely inaccurate or irrelevant\n- 0.25: Somewhat inaccurate or lacking detail\n- 0.5: Partially accurate but missing key elements\n- 0.75: Mostly accurate with minor issues\n- 1: Completely accurate and comprehensive\n\nNow, I will rate each part of the output:\n\n1. **Categories**:\n   - The categories provided (division, department, class, and My Category) accurately reflect the information in the original review. The division \"Intimates,\" department \"Intimate,\" and class \"Legwear\" are correct, and \"Leggings\" is a suitable additional category.\n   - **Rating**: 1.0\n\n2. **Sentiment**:\n   - The sentiment is correctly identified as \"positive,\" which aligns with the overall tone of the review. The reviewer expresses satisfaction with the leggings' comfort and fit.\n   - **Rating**: 1.0\n\n3. **Summary**:\n   - The summary captures the main points of the review, noting the comfort, fit after sizing down, and the mention of slight itchiness. It is concise and accurately reflects the review's content.\n   - **Rating**: 1.0\n\n4. **Personalized Message**:\n   - The personalized message is appropriate and acknowledges the reviewer's feedback positively. It addresses the comfort and fit while also recognizing the concern about itchiness, which is a nice touch.\n   - **Rating**: 1.0\n\n5. **Retail Insight**:\n   - The retail insight provides actionable suggestions, such as offering a wider range of fabric options and detailed sizing guides. These suggestions are relevant to the issues raised in the review and demonstrate an understanding of customer needs.\n   - **Rating**: 1.0\n\nOverall, the zero-shot model output is accurate and comprehensive across all evaluated categories. Here is the final evaluation in dictionary format:\n\n```json\n{\n  \"categories\": 1.0,\n  \"sentiment\": 1.0,\n  \"summary\": 1.0,\n  \"personalized_message\": 1.0,\n  \"retail_insight\": 1.0\n}\n```"
}
{
  "judge_response1": "To evaluate the zero-shot model output, I will assess each key based on its accuracy and relevance to the original review provided in the prompt. The scale for rating is as follows:\n\n- 0: Completely inaccurate or irrelevant\n- 0.1 - 0.4: Somewhat inaccurate or lacking in relevance\n- 0.5: Neutral; neither accurate nor inaccurate\n- 0.6 - 0.9: Mostly accurate and relevant, with minor issues\n- 1: Completely accurate and highly relevant\n\nNow, I will rate each part of the output:\n\n1. **Categories**:\n   - The categories provided (division, department, class, and My Category) are accurate and relevant to the review context. The addition of \"My Category\" as \"Fashion\" is appropriate given the nature of the review.\n   - **Rating**: 1.0\n\n2. **Sentiment**:\n   - The sentiment is labeled as \"neutral.\" However, the review expresses both positive (unique and fun print) and negative (dissatisfaction with length and weight) sentiments. A more accurate sentiment classification would be \"mixed\" or \"negative\" due to the expressed dissatisfaction.\n   - **Rating**: 0.4\n\n3. **Summary**:\n   - The summary accurately captures the essence of the review by mentioning the unique and fun print while also noting the dissatisfaction with the fabric's length and weight. It effectively summarizes the key points without omitting any critical information.\n   - **Rating**: 1.0\n\n4. **Personalized Message**:\n   - The personalized message is polite and acknowledges the positive aspect of the review while also addressing the concerns about the fabric's length and weight. It is appropriate and shows consideration for the customer's feedback.\n   - **Rating**: 1.0\n\n5. **Retail Insight**:\n   - The retail insight suggests offering a variety of lengths and fabric weights, which is a relevant and actionable recommendation based on the reviewer's feedback. It aligns well with the expressed dissatisfaction and provides a constructive suggestion for improvement.\n   - **Rating**: 1.0\n\nBased on the evaluations, here is the final output:\n\n```json\n{\n  \"categories\": 1.0,\n  \"sentiment\": 0.4,\n  \"summary\": 1.0,\n  \"personalized_message\": 1.0,\n  \"retail_insight\": 1.0"
}
{
  "judge_response1": "To evaluate the zero-shot model output, I will assess each key based on its accuracy and completeness in relation to the original review. The scale for rating will be as follows:\n\n- 0: Completely inaccurate or irrelevant\n- 0.25: Somewhat inaccurate or missing key elements\n- 0.5: Partially accurate but lacks detail or clarity\n- 0.75: Mostly accurate with minor issues\n- 1: Completely accurate and comprehensive\n\nNow, I will rate each part of the output:\n\n1. **Categories**:\n   - The categories provided (division, department, class, and My Category) accurately reflect the information in the original review. The addition of \"My Category\" as \"Apparel\" is appropriate and relevant.\n   - **Rating**: 1.0\n\n2. **Sentiment**:\n   - The sentiment is correctly identified as \"positive,\" which aligns with the overall tone of the review. The reviewer expresses satisfaction with the top, despite mentioning a snag.\n   - **Rating**: 1.0\n\n3. **Summary**:\n   - The summary captures the main points of the review, including the design, fit, fabric quality, and the issue with the snag. It is concise and accurately reflects the reviewer's sentiments.\n   - **Rating**: 1.0\n\n4. **Personalized Message**:\n   - The personalized message is well-crafted, acknowledging the reviewer's positive feedback while also addressing the issue with the snag. It expresses appreciation and a desire for a better future experience, which is appropriate for customer engagement.\n   - **Rating**: 1.0\n\n5. **Retail Insight**:\n   - The retail insight suggests a quality check process to minimize defects, which is a relevant and actionable recommendation based on the reviewer's experience. It addresses a specific concern raised in the review.\n   - **Rating**: 1.0\n\nBased on the evaluations, here is the final output:\n\n```json\n{\n  \"categories\": 1.0,\n  \"sentiment\": 1.0,\n  \"summary\": 1.0,\n  \"personalized_message\": 1.0,\n  \"retail_insight\": 1.0\n}\n```"
}
{
  "judge_response1": "To evaluate the zero-shot model output, I will assess each key based on its accuracy and completeness in relation to the original review. The scale for rating is as follows:\n\n- 0: Completely inaccurate or irrelevant\n- 0.25: Somewhat inaccurate or missing key elements\n- 0.5: Partially accurate but lacks detail or clarity\n- 0.75: Mostly accurate with minor issues\n- 1: Completely accurate and comprehensive\n\nNow, I will evaluate each key:\n\n1. **Categories**:\n   - The output correctly identifies the division as \"General Petite,\" the department as \"Bottoms,\" and the class as \"Jeans.\" The addition of \"My Category\" as \"Denim\" is appropriate and relevant. \n   - **Rating**: 1.0 (All categories are accurate and relevant)\n\n2. **Sentiment**:\n   - The sentiment is correctly identified as \"positive,\" which aligns with the review's overall tone and content. The reviewer expresses satisfaction with the fit, comfort, and color of the jeans.\n   - **Rating**: 1.0 (Accurate sentiment assessment)\n\n3. **Summary**:\n   - The summary captures the key points of the review: the fit being true to size, comfort, and the color's versatility for colder weather. However, it could mention the length issue noted by the reviewer, which is a significant aspect of the feedback.\n   - **Rating**: 0.75 (Mostly accurate but missing a key detail about length)\n\n4. **Personalized Message**:\n   - The personalized message is warm and acknowledges the reviewer's feedback effectively. It highlights the comfort and fit, which are the main points of the review. However, it could also address the length issue to show a more comprehensive understanding of the review.\n   - **Rating**: 0.75 (Mostly accurate but could be more comprehensive)\n\n5. **Retail Insight**:\n   - The retail insight suggests offering a petite length option, which is a relevant and actionable suggestion based on the reviewer's comment about the jeans being longer than expected. This insight is valuable for improving customer satisfaction.\n   - **Rating**: 1.0 (Accurate and actionable insight)\n\nOverall Evaluation:\n```json\n{\n  \"categories\": 1.0,\n  \"sentiment\": 1.0,\n  \"summary\": 0.75,\n  \"personalized_message\": 0.75,"
}
{
  "judge_response1": "To evaluate the zero-shot model output, I will assess each key based on its accuracy and relevance to the original review. The scale for rating will be as follows:\n\n- 0: Completely inaccurate or irrelevant\n- 0.25: Somewhat inaccurate or partially relevant\n- 0.5: Moderately accurate or relevant\n- 0.75: Mostly accurate or relevant\n- 1: Completely accurate and relevant\n\nNow, I will rate each part of the output:\n\n1. **Categories**:\n   - The categories provided (division, department, class, and My Category) are accurate and relevant to the original review. The addition of \"Casual Wear\" as a category aligns with the context of the review, which discusses a casual shirt.\n   - **Rating**: 1.0\n\n2. **Sentiment**:\n   - The sentiment is correctly identified as \"negative,\" which reflects the reviewer's dissatisfaction with the excessive fabric and crumpling issue. The overall tone of the review is indeed negative.\n   - **Rating**: 1.0\n\n3. **Summary**:\n   - The summary accurately captures the main points of the review: the cute design, the issue with excessive fabric, and the crumpling problem that affects outdoor wear. It succinctly conveys the essence of the review.\n   - **Rating**: 1.0\n\n4. **Personalized Message**:\n   - The personalized message is polite and acknowledges the feedback, which is appropriate for customer interaction. However, it could be seen as somewhat generic and not specifically tailored to the issues raised in the review (e.g., addressing the crumpling fabric directly).\n   - **Rating**: 0.75\n\n5. **Retail Insight**:\n   - The retail insight provides constructive feedback that aligns with the review's concerns. It suggests revising the design and using a more wrinkle-resistant material, which directly addresses the issues mentioned by the reviewer. This insight is practical and relevant.\n   - **Rating**: 1.0\n\nBased on the evaluations, here is the final output:\n\n```json\n{\n  \"categories\": 1.0,\n  \"sentiment\": 1.0,\n  \"summary\": 1.0,\n  \"personalized_message\": 0.75,\n  \"retail_insight\": 1.0\n}\n```"
}
{
  "judge_response1": "To evaluate the zero-shot model output, I will assess each key based on its accuracy and relevance to the original review. The scale for rating each key is as follows:\n\n- 0: Completely inaccurate or irrelevant\n- 0.25: Somewhat inaccurate or partially relevant\n- 0.5: Moderately accurate but lacking in detail or clarity\n- 0.75: Mostly accurate with minor issues\n- 1: Completely accurate and highly relevant\n\nNow, I will evaluate each part of the output:\n\n1. **Categories**:\n   - The categories provided (division, department, class, and My Category) are accurate and align with the original review. The addition of \"My Category\" as \"Blouses\" is appropriate given the context of the review.\n   - **Rating**: 1.0\n\n2. **Sentiment**:\n   - The sentiment is correctly identified as \"negative,\" which reflects the customer's disappointment with the fit and material of the blouse.\n   - **Rating**: 1.0\n\n3. **Summary**:\n   - The summary accurately captures the essence of the review, highlighting the customer's disappointment regarding the fit and material. It effectively conveys the mismatch between expectations and reality.\n   - **Rating**: 1.0\n\n4. **Personalized Message**:\n   - The personalized message is empathetic and acknowledges the customer's feedback. It expresses regret for the customer's experience and indicates that their input will be considered for future improvements. This aligns well with customer service best practices.\n   - **Rating**: 1.0\n\n5. **Retail Insight**:\n   - The retail insight provides practical suggestions for improvement, such as offering detailed sizing charts and fabric descriptions, which are relevant to the issues raised in the review. The suggestion to showcase customer photos is also a valuable idea to enhance customer expectations.\n   - **Rating**: 1.0\n\nBased on the evaluations, here is the final output:\n\n```json\n{\n  \"categories\": 1.0,\n  \"sentiment\": 1.0,\n  \"summary\": 1.0,\n  \"personalized_message\": 1.0,\n  \"retail_insight\": 1.0\n}\n```"
}
{
  "judge_response1": "To evaluate the zero-shot model output, I will assess each key based on its accuracy and completeness in relation to the original review provided. The scale for rating will be as follows:\n\n- 0: Completely inaccurate or irrelevant\n- 0.1 - 0.4: Somewhat inaccurate or lacking in relevance\n- 0.5: Neutral or average quality\n- 0.6 - 0.9: Mostly accurate and relevant, with minor issues\n- 1: Completely accurate and highly relevant\n\nNow, I will evaluate each key:\n\n1. **Categories**:\n   - The categories provided (division, department, class, and My Category) accurately reflect the information in the original review. The division \"General Petite,\" department \"Tops,\" and class \"Knits\" are all correct. The addition of \"My Category\" as \"Apparel\" is also appropriate as it encompasses the other categories.\n   - **Rating**: 1.0\n\n2. **Sentiment**:\n   - The sentiment is correctly identified as \"positive,\" which aligns with the overall tone of the review. The reviewer expresses satisfaction and enjoyment of the product, which supports this classification.\n   - **Rating**: 1.0\n\n3. **Summary**:\n   - The summary accurately captures the essence of the review, highlighting the praise for the fit, design, and the compliments received. It succinctly conveys the main points without omitting any critical information.\n   - **Rating**: 1.0\n\n4. **Personalized Message**:\n   - The personalized message is warm and appreciative, which is appropriate given the positive nature of the review. It acknowledges the feedback and emphasizes customer satisfaction, aligning well with the sentiment expressed by the reviewer.\n   - **Rating**: 1.0\n\n5. **Retail Insight**:\n   - The retail insight provides a constructive suggestion to highlight the unique design features and fit in marketing materials. This is relevant and actionable, as it directly relates to the positive aspects mentioned in the review. It effectively connects the review to potential marketing strategies.\n   - **Rating**: 1.0\n\nOverall, the zero-shot model output is accurate and complete across all evaluated categories. \n\nFinal Evaluation:\n```json\n{\n  \"categories\": 1.0,\n  \"sentiment\": 1.0,\n  \"summary\": 1.0,\n  \"personalized_message\": 1.0,"
}
{
  "judge_response1": "To evaluate the zero-shot model output, I will assess each key based on its accuracy and relevance to the original review. The scale for rating each key is as follows:\n\n- 0: Completely inaccurate or irrelevant\n- 0.25: Somewhat inaccurate or partially relevant\n- 0.5: Moderately accurate but lacking in detail or clarity\n- 0.75: Mostly accurate with minor issues\n- 1: Completely accurate and relevant\n\nNow, I will rate each part of the output:\n\n1. **Categories**:\n   - The categories provided (division, department, class, and My Category) accurately reflect the information in the original review. The addition of \"My Category\" as \"Shirts\" is appropriate since the review is about a shirt.\n   - **Rating**: 1.0\n\n2. **Sentiment**:\n   - The sentiment is correctly identified as \"positive,\" which aligns with the customer's satisfaction regarding the fit and comfort of the shirt.\n   - **Rating**: 1.0\n\n3. **Summary**:\n   - The summary accurately captures the essence of the review, noting the customer's satisfaction with the fit and comfort while also mentioning the desire for more color options. It is concise and relevant.\n   - **Rating**: 1.0\n\n4. **Personalized Message**:\n   - The personalized message is appropriate and acknowledges the customer's feedback positively. It also addresses the suggestion for more colors, which is relevant to the review.\n   - **Rating**: 1.0\n\n5. **Retail Insight**:\n   - The retail insight is insightful and actionable, suggesting that expanding the color range could enhance customer satisfaction and encourage repeat purchases. This aligns well with the customer's expressed desire for more color options.\n   - **Rating**: 1.0\n\nBased on the evaluations, here is the final output:\n\n```json\n{\n  \"categories\": 1.0,\n  \"sentiment\": 1.0,\n  \"summary\": 1.0,\n  \"personalized_message\": 1.0,\n  \"retail_insight\": 1.0\n}\n```"
}
{
  "judge_response1": "To evaluate the zero-shot model output, I will assess each key based on its accuracy and completeness in relation to the original review provided. The scale for rating will be as follows:\n\n- 0: Completely inaccurate or irrelevant\n- 0.25: Somewhat inaccurate or lacking detail\n- 0.5: Partially accurate but missing key elements\n- 0.75: Mostly accurate with minor issues\n- 1: Completely accurate and comprehensive\n\nNow, I will evaluate each part of the output:\n\n1. **Categories**:\n   - The categories provided (division, department, class, and My Category) accurately reflect the information in the original prompt. The addition of \"My Category\" as \"Sweaters\" is appropriate given the context of the review.\n   - **Rating**: 1.0\n\n2. **Sentiment**:\n   - The sentiment is correctly identified as \"positive,\" which aligns with the enthusiastic tone of the review. The reviewer expresses love for the sweater and recommends it, indicating a positive sentiment.\n   - **Rating**: 1.0\n\n3. **Summary**:\n   - The summary accurately captures the essence of the review, mentioning the reviewer's satisfaction, the sweater's versatility, softness, and durability. It succinctly reflects the key points made in the review.\n   - **Rating**: 1.0\n\n4. **Personalized Message**:\n   - The personalized message is well-crafted and expresses gratitude for the review. It acknowledges the reviewer's positive experience and highlights the sweater's versatility, which is a key point in the review. However, it could be slightly more personalized by mentioning the specific attributes (softness and durability) that the reviewer appreciated.\n   - **Rating**: 0.75\n\n5. **Retail Insight**:\n   - The retail insight is thoughtful and relevant, suggesting an expansion of colors and styles based on the sweater's versatility. This aligns with the review's emphasis on the sweater's adaptability for different occasions. It provides actionable advice for potential product development.\n   - **Rating**: 1.0\n\nBased on the evaluations, here is the final output:\n\n```json\n{\n  \"categories\": 1.0,\n  \"sentiment\": 1.0,\n  \"summary\": 1.0,\n  \"personalized_message\": 0.75,\n  \"retail_insight\": 1.0\n}\n```"
}
{
  "judge_response1": "To evaluate the zero-shot model output, I will assess each key based on its accuracy and completeness in relation to the original review provided. The scale for rating each key is as follows:\n\n- 0: Completely inaccurate or missing information.\n- 0.25: Some relevant information, but largely inaccurate or incomplete.\n- 0.5: Partially accurate, but missing key details or has significant inaccuracies.\n- 0.75: Mostly accurate with minor issues or omissions.\n- 1: Fully accurate and complete.\n\nNow, I will evaluate each key:\n\n1. **Categories**:\n   - The categories provided (division, department, class, and an additional category \"My Category\") are accurate and align with the original review. The division, department, and class match the information given in the prompt.\n   - **Rating**: 1.0\n\n2. **Sentiment**:\n   - The sentiment is correctly identified as \"positive,\" which reflects the overall tone of the review. The reviewer expresses love for the top and highlights its positive attributes.\n   - **Rating**: 1.0\n\n3. **Summary**:\n   - The summary accurately captures the main points of the review: the reviewer's love for the top, its softness, flattering fit, versatility, and the concern about it being dry clean only. It is concise and covers all essential aspects.\n   - **Rating**: 1.0\n\n4. **Personalized Message**:\n   - The personalized message is appropriate and acknowledges the reviewer's positive feedback while addressing the concern about dry cleaning. It expresses appreciation and indicates that the feedback will be considered, which is a good customer service approach.\n   - **Rating**: 1.0\n\n5. **Retail Insight**:\n   - The retail insight suggests offering a machine-washable version of the top, which is a relevant and actionable suggestion based on the reviewer's concern about the dry cleaning requirement. This insight is practical and aligns well with the feedback provided.\n   - **Rating**: 1.0\n\nBased on the evaluations, here is the final output:\n\n```json\n{\n  \"categories\": 1.0,\n  \"sentiment\": 1.0,\n  \"summary\": 1.0,\n  \"personalized_message\": 1.0,\n  \"retail_insight\": 1.0\n}\n```"
}
{
  "judge_response1": "To evaluate the zero-shot model output, I will assess each key based on its accuracy and completeness in relation to the original review. The scale for rating is as follows:\n\n- 0: Completely inaccurate or irrelevant\n- 0.25: Somewhat inaccurate or missing key elements\n- 0.5: Partially accurate but lacks detail or clarity\n- 0.75: Mostly accurate with minor issues\n- 1: Completely accurate and comprehensive\n\nNow, I will rate each part of the output:\n\n1. **Categories**:\n   - The categories provided (division, department, class, and My Category) accurately reflect the information in the original review. The addition of \"My Category\" as \"Tunic\" is appropriate given the context.\n   - **Rating**: 1.0\n\n2. **Sentiment**:\n   - The sentiment is correctly identified as \"positive,\" which aligns with the overall tone of the review. The reviewer expresses satisfaction and positive feelings about the tunic.\n   - **Rating**: 1.0\n\n3. **Summary**:\n   - The summary captures the main points of the review, including the customer's satisfaction, the flattering fit, the heavy fabric, and the mention of the high slit. It is concise and accurately reflects the review's content.\n   - **Rating**: 1.0\n\n4. **Personalized Message**:\n   - The personalized message is warm and acknowledges the customer's feedback. It also addresses the slit issue, which is a relevant point from the review. However, it could be slightly more tailored by mentioning the specific features the customer liked (e.g., fabric weight).\n   - **Rating**: 0.75\n\n5. **Retail Insight**:\n   - The retail insight provides a useful suggestion about enhancing product descriptions and leveraging customer reviews. It is relevant and actionable, but it could be more specific about how to address the high slit issue mentioned in the review.\n   - **Rating**: 0.75\n\nNow, compiling the ratings into a dictionary:\n\n```json\n{\n  \"categories\": 1.0,\n  \"sentiment\": 1.0,\n  \"summary\": 1.0,\n  \"personalized_message\": 0.75,\n  \"retail_insight\": 0.75\n}\n```"
}
{
  "judge_response1": "To evaluate the zero-shot model output, I will assess each key based on its accuracy and relevance to the original review. The scale for rating is as follows:\n\n- 0: Completely inaccurate or irrelevant\n- 0.25: Somewhat inaccurate or partially relevant\n- 0.5: Moderately accurate but lacks detail or clarity\n- 0.75: Mostly accurate with minor issues\n- 1: Completely accurate and highly relevant\n\nNow, I will rate each part of the output:\n\n1. **Categories**:\n   - The categories provided (\"division\", \"department\", \"class\", and \"My Category\") are accurate and relevant to the original review. The addition of \"My Category\" as \"Petite Fashion\" is a reasonable extrapolation based on the context of the review.\n   - **Rating**: 1.0\n\n2. **Sentiment**:\n   - The sentiment is correctly identified as \"positive,\" reflecting the customer's satisfaction with the dress's fit and appearance.\n   - **Rating**: 1.0\n\n3. **Summary**:\n   - The summary accurately captures the essence of the review, noting that the dress fits well and flatters the customer's body shape. It also appropriately mentions the petite frame, which is relevant to the review.\n   - **Rating**: 1.0\n\n4. **Personalized Message**:\n   - The personalized message is warm and appreciative, aligning well with the positive sentiment of the review. It acknowledges the customer's feedback and expresses enthusiasm about their satisfaction.\n   - **Rating**: 1.0\n\n5. **Retail Insight**:\n   - The retail insight suggests expanding the range of sizes and styles for different body shapes within the petite category. This is a thoughtful recommendation that aligns with the review's context, as the customer mentions specific body shape characteristics.\n   - **Rating**: 1.0\n\nOverall, the zero-shot model output is accurate and relevant across all categories, sentiment, summary, personalized message, and retail insight. \n\nFinal Evaluation:\n```json\n{\n  \"categories\": 1.0,\n  \"sentiment\": 1.0,\n  \"summary\": 1.0,\n  \"personalized_message\": 1.0,\n  \"retail_insight\": 1.0\n}\n```"
}
{
  "judge_response1": "To evaluate the zero-shot model output, I will assess each key based on its accuracy and relevance to the original review. The scale for rating each key is as follows:\n\n- 0: Completely inaccurate or irrelevant\n- 0.25: Somewhat inaccurate or partially relevant\n- 0.5: Moderately accurate but lacking detail or clarity\n- 0.75: Mostly accurate with minor issues\n- 1: Completely accurate and highly relevant\n\nNow, I will rate each part of the output:\n\n1. **Categories**:\n   - The categories provided (division, department, class, and My Category) accurately reflect the information in the original review. The addition of \"My Category\" as \"Apparel\" is appropriate and relevant.\n   - **Rating**: 1.0\n\n2. **Sentiment**:\n   - The sentiment is correctly identified as \"positive,\" reflecting the customer's overall satisfaction with the product after resolving the sizing issue.\n   - **Rating**: 1.0\n\n3. **Summary**:\n   - The summary captures the essence of the review, noting the fun aspect of the top and the sizing issue that was resolved. It is concise and accurately reflects the customer's experience.\n   - **Rating**: 1.0\n\n4. **Personalized Message**:\n   - The personalized message is warm and acknowledges the customer's feedback, expressing happiness about the fit and design. It aligns well with the positive sentiment of the review.\n   - **Rating**: 1.0\n\n5. **Retail Insight**:\n   - The retail insight is practical and relevant, suggesting improvements in sizing information to reduce exchanges. This is a valuable recommendation based on the customer's experience.\n   - **Rating**: 1.0\n\nOverall, the zero-shot model output is accurate and complete in all aspects based on the original review. \n\nFinal Evaluation:\n```json\n{\n  \"categories\": 1.0,\n  \"sentiment\": 1.0,\n  \"summary\": 1.0,\n  \"personalized_message\": 1.0,\n  \"retail_insight\": 1.0\n}\n```"
}
{
  "judge_response1": "To evaluate the zero-shot model output, I will assess each key based on its accuracy and relevance to the original review. The scale for rating each key is as follows:\n\n- 0: Completely inaccurate or irrelevant\n- 0.25: Somewhat inaccurate or partially relevant\n- 0.5: Moderately accurate but lacking in detail or clarity\n- 0.75: Mostly accurate with minor issues\n- 1: Completely accurate and relevant\n\nNow, I will rate each part of the output:\n\n1. **Categories**:\n   - The categories provided (\"division\", \"department\", \"class\", and \"My Category\") are accurate and align with the original review's context. The addition of \"My Category\" as \"Lounge Wear\" is appropriate given the context of the review.\n   - **Rating**: 1.0\n\n2. **Sentiment**:\n   - The sentiment is labeled as \"neutral,\" which is appropriate given the review's mixed feelings about the product. The reviewer expresses both positive and negative aspects, making \"neutral\" a fitting classification.\n   - **Rating**: 1.0\n\n3. **Summary**:\n   - The summary accurately captures the main points of the review, including the positive note about the length and the negative aspects regarding the fit and potential return. It succinctly conveys the reviewer's overall sentiment.\n   - **Rating**: 1.0\n\n4. **Personalized Message**:\n   - The personalized message is thoughtful and acknowledges the reviewer's feedback. It expresses appreciation for the input and highlights the suggestion for a petite size, which is relevant to the review. However, it could be slightly more tailored to the specific issues raised (e.g., mentioning the waist fit).\n   - **Rating**: 0.75\n\n5. **Retail Insight**:\n   - The retail insight is practical and directly addresses the reviewer's concern about sizing by suggesting the expansion of the size range and the provision of detailed sizing guides. This is a relevant and actionable recommendation based on the review.\n   - **Rating**: 1.0\n\nNow, compiling the ratings into a dictionary format:\n\n```json\n{\n  \"categories\": 1.0,\n  \"sentiment\": 1.0,\n  \"summary\": 1.0,\n  \"personalized_message\": 0.75,\n  \"retail_insight\": 1.0\n}\n```"
}
{
  "judge_response1": "To evaluate the zero-shot model output, I will assess each key based on its accuracy and relevance to the original review. The scale for rating is as follows:\n\n- 0: Completely inaccurate or irrelevant\n- 0.25: Somewhat inaccurate or partially relevant\n- 0.5: Neutral; neither accurate nor inaccurate\n- 0.75: Mostly accurate with minor issues\n- 1: Completely accurate and relevant\n\nNow, I will rate each part of the output:\n\n1. **Categories**:\n   - The categories provided (division, department, class, and My Category) accurately reflect the information in the original review. The addition of \"My Category\" as \"Pilcro Jeans\" is appropriate as it specifies the brand mentioned in the review.\n   - **Rating**: 1.0\n\n2. **Sentiment**:\n   - The sentiment is labeled as \"neutral.\" However, the review expresses both positive feelings about the stretch and fit of the jeans and a negative concern regarding pilling. A more accurate sentiment might be \"mixed\" or \"positive with concerns.\"\n   - **Rating**: 0.5\n\n3. **Summary**:\n   - The summary captures the main points of the review: appreciation for the stretch and concern about pilling. It is concise and accurately reflects the review's content.\n   - **Rating**: 1.0\n\n4. **Personalized Message**:\n   - The personalized message acknowledges the reviewer's feedback and addresses both the positive and negative aspects mentioned. It is warm and appropriate, making the customer feel valued.\n   - **Rating**: 1.0\n\n5. **Retail Insight**:\n   - The retail insight suggests improving fabric quality in the inner thigh area to reduce pilling, which is a relevant and actionable suggestion based on the review. It addresses a specific concern raised by the reviewer.\n   - **Rating**: 1.0\n\nBased on the evaluations, here is the final output:\n\n```json\n{\n  \"categories\": 1.0,\n  \"sentiment\": 0.5,\n  \"summary\": 1.0,\n  \"personalized_message\": 1.0,\n  \"retail_insight\": 1.0\n}\n```"
}
{
  "judge_response1": "To evaluate the zero-shot model output, I will assess each key based on its accuracy and relevance to the original review. The scale for rating is as follows:\n\n- 0: Completely inaccurate or irrelevant\n- 0.25: Somewhat inaccurate or partially relevant\n- 0.5: Moderately accurate but lacking in detail or clarity\n- 0.75: Mostly accurate with minor issues\n- 1: Completely accurate and highly relevant\n\nNow, I will rate each part of the output:\n\n1. **Categories**:\n   - The categories provided (division, department, class, and an additional category of \"Fashion\") are accurate and relevant to the review. The original review clearly indicates that it pertains to a sweater in the tops division, and the addition of \"Fashion\" is appropriate given the context.\n   - **Rating**: 1.0\n\n2. **Sentiment**:\n   - The sentiment is correctly identified as \"positive.\" The review expresses enjoyment and satisfaction with the sweater, which aligns with a positive sentiment.\n   - **Rating**: 1.0\n\n3. **Summary**:\n   - The summary accurately captures the essence of the review, noting the reviewer's appreciation for the stylishness and non-bulky nature of the sweater, as well as the mention of great material. It succinctly reflects the key points made in the review.\n   - **Rating**: 1.0\n\n4. **Personalized Message**:\n   - The personalized message is warm and engaging, thanking the reviewer for their feedback and expressing enthusiasm about their satisfaction with the sweater. It aligns well with the positive sentiment of the review and encourages continued engagement.\n   - **Rating**: 1.0\n\n5. **Retail Insight**:\n   - The retail insight is relevant and actionable, suggesting an expansion of the range of lightweight, stylish sweaters. This aligns with the reviewer's preference for less bulk while maintaining style, indicating a good understanding of customer needs.\n   - **Rating**: 1.0\n\nBased on the evaluations, here is the final output:\n\n```json\n{\n  \"categories\": 1.0,\n  \"sentiment\": 1.0,\n  \"summary\": 1.0,\n  \"personalized_message\": 1.0,\n  \"retail_insight\": 1.0\n}\n```"
}
{
  "judge_response1": "To evaluate the zero-shot model output, I will assess each key based on its accuracy and relevance to the original review. The scale for rating each key is as follows:\n\n- 0: Completely inaccurate or irrelevant\n- 0.25: Somewhat inaccurate or partially relevant\n- 0.5: Neutral; neither accurate nor inaccurate\n- 0.75: Mostly accurate but with minor issues\n- 1: Completely accurate and relevant\n\nNow, I will rate each part of the output:\n\n1. **Categories**:\n   - The categories provided (division, department, class, and My Category) accurately reflect the information in the original review. The division is \"General,\" the department is \"Bottoms,\" and the class is \"Jeans,\" which are all correct. The additional category \"Apparel\" is also appropriate as it encompasses the other categories.\n   - **Rating**: 1.0\n\n2. **Sentiment**:\n   - The sentiment is labeled as \"negative,\" which is accurate given the reviewer's disappointment with the stitching and discomfort. The reviewer expresses a positive sentiment towards the fabric and style but ultimately conveys a negative experience due to discomfort.\n   - **Rating**: 1.0\n\n3. **Summary**:\n   - The summary captures the essence of the review well. It mentions the appreciation for the fabric quality and the disappointment regarding the knee stitching, which causes discomfort. However, it could have included the reviewer's love for the style, which is a significant part of the review.\n   - **Rating**: 0.75\n\n4. **Personalized Message**:\n   - The personalized message is empathetic and acknowledges both the positive aspect (love for the style) and the negative aspect (discomfort from the knee stitching). It effectively conveys appreciation for the feedback, which is appropriate for customer engagement.\n   - **Rating**: 1.0\n\n5. **Retail Insight**:\n   - The retail insight provides a constructive suggestion to revise the stitching design to enhance comfort, which is relevant to the review's main issue. It addresses the discomfort experienced by the customer and suggests a way to improve customer satisfaction, making it a valuable insight.\n   - **Rating**: 1.0\n\nBased on the evaluations, here is the final output:\n\n```json\n{\n  \"categories\": 1.0,\n  \"sentiment\": 1.0,\n  \"summary\": 0.75,"
}

import json
import re
import pandas as pd # Ensure pandas is imported if not already

# Helper function to extract descriptive text for each category
def extract_description_for_category1(full_response_text1, start_pattern1, end_pattern1):
    # Regex to find the text between start_pattern and end_pattern
    # Using re.escape for special characters in patterns and re.DOTALL for multiline match
    match = re.search(re.escape(start_pattern1) + r'\s*\n(.*?)\n\s*' + re.escape(end_pattern1), full_response_text1, re.DOTALL)
    if match:
        description1 = match.group(1).strip()
        # Remove the "**Rating**: X.X" line from the end of the description
        description1 = re.sub(r'\s*\*\*Rating\*\*:\s*\d+\.\d+', '', description1).strip()
        return description1
    return "Description not found"

# Define the patterns for each section (start and end markers)
patterns_to_extract1 = [
    ("Categories_Description", "1. **Categories**:", "2. **Sentiment**:"),
    ("Sentiment_Description", "2. **Sentiment**:", "3. **Summary**:"),
    ("Summary_Description", "3. **Summary**:", "4. **Personalized Message**:"),
    ("Personalized_Message_Description", "4. **Personalized Message**:", "5. **Retail Insight**:"),
    ("Retail_Insight_Description", "5. **Retail Insight**:", "Overall Evaluation:"), # Note: Adjusted end pattern to capture until "Overall Evaluation"
]


eval_results_parsed_v21 = []
descriptive_results_v21 = [] # New list for descriptive text

for index1, row in few_shot_results_df1.iterrows():
  evaluation_result_for_row1 = llm_as_judge_few(
    llm_output_to_judge1 = row['raw_llm_output1'],
    original_user_prompt_string_from_few_shot1 = row['original_user_content1']
  )

  judge_response_str1 = evaluation_result_for_row1['judge_response1']

  # Extract numerical scores
  match_scores1 = re.search(r'```json\n(.*?)```', judge_response_str1, re.DOTALL)
  if match_scores1:
      json_str1 = match_scores1.group(1)
      try:
          scores1 = json.loads(json_str1)
          eval_results_parsed_v21.append(scores1)
      except json.JSONDecodeError:
          print(f"Could not decode JSON for scores from: {json_str1}")
          eval_results_parsed_v21.append({}) # Append empty dict on error
  else:
      print(f"No JSON block found for scores in: {judge_response_str1}")
      eval_results_parsed_v21.append({}) # Append empty dict if no JSON block

  # Extract descriptive text
  extracted_descriptions1 = {}
  for name1, start_pat1, end_pat1 in patterns_to_extract1:
      extracted_descriptions1[name1] = extract_description_for_category1(judge_response_str1, start_pat1, end_pat1)
  descriptive_results_v21.append(extracted_descriptions1)

# Create DataFrame from numerical scores (as before)
eval_df_v21 = pd.DataFrame(eval_results_parsed_v21)

# Create a new DataFrame from descriptive texts
eval_df_descriptive_v21 = pd.DataFrame(descriptive_results_v21)

# Display the DataFrame with numerical scores
print("\nNumerical Scores for Few-Shot V21 (Head):")
display(eval_df_v21.head())
print("\nAverage Numerical Scores for Few-Shot V21:")
print(eval_df_v21.mean())

# Display the DataFrame with descriptive texts
print("\nDescriptive Explanations for Few-Shot V21 (Head1):")
display(eval_df_descriptive_v21.head())

No JSON block found for scores in: To evaluate the zero-shot model output, I will assess each key based on its accuracy and completeness in relation to the original review. The scale for rating each key is as follows:

- 0: Completely inaccurate or irrelevant
- 0.25: Somewhat inaccurate or missing key elements
- 0.5: Partially accurate but lacks detail or clarity
- 0.75: Mostly accurate with minor issues
- 1: Fully accurate and comprehensive

Now, I will rate each part of the output:

1. **Categories**:
   - The categories provided (division, department, class, and My Category) are accurate and relevant to the review. The addition of "My Category" as "Apparel" is appropriate as it encompasses the other categories.
   - **Rating**: 1.0

2. **Sentiment**:
   - The sentiment is labeled as "neutral," which is somewhat misleading. The review contains mixed feelings: the reviewer mentions the dress runs large and has a scratchy material, but also appreciates the color and the stylist's opinion. A more accurate sentiment might be "mixed" or "slightly positive."
   - **Rating**: 0.5

3. **Summary**:
   - The summary captures the main points of the review: the dress runs large, the material is scratchy, the color is appealing, and it looks nice on the stylist. However, it could mention the issue with the zipper and the reviewer's indecision about keeping the dress for a more complete picture.
   - **Rating**: 0.75

4. **Personalized Message**:
   - The personalized message is appropriate and acknowledges the feedback. It addresses the sizing and material concerns while also expressing appreciation for the positive aspects mentioned. However, it could be slightly more tailored by referencing the zipper issue.
   - **Rating**: 0.75

5. **Retail Insight**:
   - The retail insight is practical and relevant, suggesting improvements in sizing charts, fabric descriptions, and addressing zipper functionality. This aligns well with the review's content and provides actionable advice for the retailer.
   - **Rating**: 1.0

Based on the evaluations, here is the final output:

```json
{
  "categories": 1.0,
  "sentiment": 0.5,
  "summary": 0.75,
  "personalized_message": 0.75,
No JSON block found for scores in: To evaluate the zero-shot model output, I will assess each key based on its accuracy and relevance to the original review. The scale for rating will be as follows:

- 0: Completely inaccurate or irrelevant
- 0.25: Somewhat inaccurate or partially relevant
- 0.5: Moderately accurate or relevant, but with significant issues
- 0.75: Mostly accurate or relevant, with minor issues
- 1: Completely accurate and relevant

Now, I will rate each part of the output:

1. **Categories**:
   - The categories provided (division, department, class, My Category) are accurate and align with the original review. The addition of "My Category" as "Apparel" is appropriate as it encompasses the dress category.
   - **Rating**: 1.0

2. **Sentiment**:
   - The sentiment is labeled as "negative," which accurately reflects the reviewer's dissatisfaction with the fit of the dress, particularly for larger chests. The review expresses frustration and disappointment, supporting the negative sentiment classification.
   - **Rating**: 1.0

3. **Summary**:
   - The summary captures the essence of the review well, highlighting the high quality and design of the dress while also addressing the fit issues for larger chests. It correctly notes that these issues make the dress unsuitable for work, which is a key point in the review.
   - **Rating**: 1.0

4. **Personalized Message**:
   - The personalized message is thoughtful and acknowledges the feedback provided by the reviewer. It expresses appreciation for the insights regarding fit, which is relevant to the review. However, it could be slightly more personalized by mentioning the specific issue of the chest fit.
   - **Rating**: 0.75

5. **Retail Insight**:
   - The retail insight is practical and directly addresses the issues raised in the review. It suggests offering a wider range of sizes and styles for different body types, which is a relevant recommendation based on the reviewer's experience. The suggestion for detailed fit guides is also appropriate.
   - **Rating**: 1.0

Now, I will compile the evaluation into a dictionary format:

```json
{
  "categories": 1.0,
  "sentiment": 1.0,
  "summary": 1.0,
  "personalized_message": 0.75,
  "ret
No JSON block found for scores in: To evaluate the zero-shot model output, I will assess each key based on its accuracy and completeness in relation to the original review provided in the prompt. The scale for evaluation is as follows:

- 0: Completely inaccurate or irrelevant
- 0.25: Somewhat inaccurate or lacking detail
- 0.5: Partially accurate but missing key elements
- 0.75: Mostly accurate with minor issues
- 1: Completely accurate and comprehensive

Now, I will rate each part of the output:

1. **Categories**:
   - The categories provided (division, department, class, and My Category) accurately reflect the information in the original review. "Intimates" and "Intimate" are correctly identified, and "Legwear" aligns with the class of the product. The addition of "My Category" as "Leggings" is appropriate and relevant.
   - **Rating**: 1.0

2. **Sentiment**:
   - The sentiment is correctly identified as "positive," which aligns with the overall tone of the review. The reviewer expresses satisfaction with the leggings, indicating a positive experience.
   - **Rating**: 1.0

3. **Summary**:
   - The summary captures the main points of the review, noting the comfort, fit after sizing down, and the mention of slight itchiness. It is concise and accurately reflects the review's content.
   - **Rating**: 1.0

4. **Personalized Message**:
   - The personalized message is well-crafted, expressing gratitude for the feedback and acknowledging the positive aspects of the review. It also addresses the concern about itchiness, which shows attentiveness to the reviewer's experience. However, it could be slightly more personalized by mentioning the specific issue of leggings falling down, which was a significant point in the review.
   - **Rating**: 0.75

5. **Retail Insight**:
   - The retail insight provides actionable suggestions, such as offering a wider range of fabric options and providing detailed sizing guides. These suggestions are relevant to the issues raised in the review and demonstrate an understanding of customer needs. However, it could be enhanced by specifically mentioning the issue of leggings falling down, which was a key concern for the reviewer.
   - **Rating**: 0.75

Overall Evaluation:
```json
{
  "categories": 1.0,
  "sentiment": 1.0,

Numerical Scores for Few-Shot V21 (Head):

Average Numerical Scores for Few-Shot V21:
categories              0.994681
sentiment               0.929787
summary                 0.994681
personalized_message    0.907447
retail_insight          0.989362
dtype: float64

Descriptive Explanations for Few-Shot V21 (Head1):

# Display the DataFrame with numerical scores
print("\nNumerical Scores for Few-Shot V21 (Head):")
display(eval_df_v21.head(20))
print("\nAverage Numerical Scores for Few-Shot V21:")
print(eval_df_v21.mean())

# Display the DataFrame with descriptive texts
print("\nDescriptive Explanations for Few-Shot V21 (Head1):")
display(eval_df_descriptive_v21.head(20))

Numerical Scores for Few-Shot V21 (Head):

Average Numerical Scores for Few-Shot V21:
categories              0.994681
sentiment               0.929787
summary                 0.994681
personalized_message    0.907447
retail_insight          0.989362
dtype: float64

Descriptive Explanations for Few-Shot V21 (Head1):

SYSTEM_MESSAGE_COT0 = """
You are a strict, expert retail product review analyst. Prepare a short report
with the following element, outputting the response in JSON format:
The categories: this should be an object with three entries: 'division', 'department', 'class' and 'My Category'.
Use this format:

Categories:
   Division: General
   Department: Tops
   Class: Knits
   My category: Possible Female Shopper
"""

BASE_MESSAGES_COT0 = [
    {"role": "system", "content": SYSTEM_MESSAGE_COT0},
]

SYSTEM_MESSAGE_COT1 = """
You are a strict, expert retail product review analyst. Prepare a short report
with the following element, outputting the response in JSON format:
The sentiment: this should be a string indicating 'positive', 'neutral', or
'negative'. If more than one sentiment applies, prioritize the dominant one.

Sentiment: positive
"""

BASE_MESSAGES_COT01 = [
    {"role": "system", "content": SYSTEM_MESSAGE_COT1},
]

SYSTEM_MESSAGE_COT2 = """
You are a strict, expert retail product review analyst. Prepare a short report
with the following element, outputting the response in JSON format:
The summary: this is a short summary of the review.

Summary: The reviewer appreciates the softness and stylish design of the
pullover, noting its accurate fit and receiving compliments on it.
"""

BASE_MESSAGES_COT02 = [
    {"role": "system", "content": SYSTEM_MESSAGE_COT2},
]

SYSTEM_MESSAGE_COT3 = """
You are a strict, expert retail product review analyst. Prepare a short report
with the following element, outputting the response in JSON format:
The personalized_message: a personalized message composed in response to the review.

Personalized_message:"Thank you for your wonderful feedback! We're thrilled
to hear that you love the softness and style of your new pullover. Enjoy wearing it!
"""

BASE_MESSAGES_COT03 = [
    {"role": "system", "content": SYSTEM_MESSAGE_COT3},
]

SYSTEM_MESSAGE_COT4 = """
You are a strict, expert retail product review analyst. Prepare a short report
with the following element, outputting the response in JSON format:
  The retail_insight: any insights that can improve customer satisfaction.

  Retail_insight:
  Consider highlighting the softness and stylish design in
  marketing materials, as these features resonate well with customers. Additionally,
  showcasing customer fit examples could help potential buyers make informed decisions.
  """

BASE_MESSAGES_COT04 = [
    {"role": "system", "content": SYSTEM_MESSAGE_COT4},
]

#imput for llm-as-a-judge
def review_prompt_five_COT3():
    """
    Samples five reviews and returns them as a list of dictionaries,
    each representing a single review.
    """
    five_samples_df = reviews4.sample(n=50) # Get five random rows as a DataFrame
    list_of_review_dicts = []
    for index, row in five_samples_df.iterrows():
        list_of_review_dicts.append({
            "div_name": row['Division.Name'],
            "dept_name": row['Department.Name'],
            "class_name": row['Class.Name'],
            "review_text": row['Review.Text'],
            "review_index": row['index']
        })
    return list_of_review_dicts

def generate_cot_output(model_name: str = "gpt-4o-mini", base_message_cot: list = None, review_data_single: dict = None) -> tuple:
    """
    Generates a Chain-of-Thought prompt response based on the provided single review data
    and a specified base message for a particular aspect (e.g., categories, sentiment).
    Returns the raw LLM output and the review index.
    """
    if review_data_single is None:
        raise ValueError("review_data_single cannot be None.")
    if base_message_cot is None:
        raise ValueError("base_message_cot cannot be None.")

    user_content = (
        f"Division: {review_data_single['div_name']}\n"
        f"Department: {review_data_single['dept_name']}\n"
        f"Class: {review_data_single['class_name']}\n"
        f"Review: {review_data_single['review_text']}"
        f"Index: {review_data_single['review_index']}"
    )

    instructions = base_message_cot + [{
        "role": "user",
        "content": user_content
    }]

    response_COT = client.chat.completions.create(
        model=model_name,
        messages=instructions,
        temperature=0.0,
        max_tokens=50
    )

    raw_output = response_COT.choices[0].message.content.strip()
    #print(f"This is the actual review:\n{review_data_single['review_text']}\n")

    return {'raw_output': raw_output}, review_data_single['review_index']

import pandas as pd # Ensure pandas is imported

# Get the list of five review samples for CoT
list_of_cot_review_samples = review_prompt_five_COT3()

all_cot_v1_outputs = []

for i, review_data in enumerate(list_of_cot_review_samples):
    print(f"\n----- Processing Review {i+1} (Original Index: {review_data['review_index']}) -----")

    # Categories
    categories_output, _ = generate_cot_output(base_message_cot=BASE_MESSAGES_COT0, review_data_single=review_data)
    print("Categories Output:\n", categories_output['raw_output'], "\n")

    # Sentiment
    sentiment_output, _ = generate_cot_output(base_message_cot=BASE_MESSAGES_COT01, review_data_single=review_data)
    print("Sentiment Output:\n", sentiment_output['raw_output'], "\n")

    # Summary
    summary_output, _ = generate_cot_output(base_message_cot=BASE_MESSAGES_COT02, review_data_single=review_data)
    print("Summary Output:\n", summary_output['raw_output'], "\n")

    # Personalized Message
    pm_output, _ = generate_cot_output(base_message_cot=BASE_MESSAGES_COT03, review_data_single=review_data)
    print("Personalized Message Output:\n", pm_output['raw_output'], "\n")

    # Retail Insight
    ri_output, _ = generate_cot_output(base_message_cot=BASE_MESSAGES_COT04, review_data_single=review_data)
    print("Retail Insight Output:\n", ri_output['raw_output'], "\n")

    # Construct the original user content string for the judge
    original_user_content_str = (
        f"Division: {review_data['div_name']}\n"
        f"Department: {review_data['dept_name']}\n"
        f"Class: {review_data['class_name']}\n"
        f"Review: {review_data['review_text']}"
        f"Index: {review_data['review_index']}"
    )

    all_cot_v1_outputs.append({
        "review_index": review_data['review_index'],
        "categories_raw_output": categories_output['raw_output'],
        "sentiment_raw_output": sentiment_output['raw_output'],
        "summary_raw_output": summary_output['raw_output'],
        "personalized_message_raw_output": pm_output['raw_output'],
        "retail_insight_raw_output": ri_output['raw_output'],
        "original_user_content_cot": original_user_content_str # Add this new column
    })

cot_results_df = pd.DataFrame(all_cot_v1_outputs)
print("\n--- Consolidated COT V1 Results (First 5 Rows) ---")
print(cot_results_df.head())

----- Processing Review 1 (Original Index: 11956) -----
This is the actual review:
I liked this shirt so much i ordered a second one online in the same size but different color. unfortunately the second one is much shorter than the first and looks less like a tunic or more like a cropped flowy top - not a good look. so the second one is going back. otherwise the shirt would get 5 stars. washes well, super soft and comfy, and looks great with all kinds of bottoms...jeans, cords, leggings. wish it came in more colors. i typically wear a m or l in retailer tops and took the l in th

Categories Output:
 ```json
{
  "Categories": {
    "Division": "General",
    "Department": "Tops",
    "Class": "Knits",
    "My Category": "Possible Female Shopper"
  },
  "Review": { 

This is the actual review:
I liked this shirt so much i ordered a second one online in the same size but different color. unfortunately the second one is much shorter than the first and looks less like a tunic or more like a cropped flowy top - not a good look. so the second one is going back. otherwise the shirt would get 5 stars. washes well, super soft and comfy, and looks great with all kinds of bottoms...jeans, cords, leggings. wish it came in more colors. i typically wear a m or l in retailer tops and took the l in th

Sentiment Output:
 ```json
{
  "sentiment": "neutral"
}
``` 

This is the actual review:
I liked this shirt so much i ordered a second one online in the same size but different color. unfortunately the second one is much shorter than the first and looks less like a tunic or more like a cropped flowy top - not a good look. so the second one is going back. otherwise the shirt would get 5 stars. washes well, super soft and comfy, and looks great with all kinds of bottoms...jeans, cords, leggings. wish it came in more colors. i typically wear a m or l in retailer tops and took the l in th

Summary Output:
 ```json
{
  "summary": "The reviewer loves the softness and comfort of the shirt, leading to a second purchase, but is disappointed with the fit of the second shirt, which is shorter than expected. Overall, the first shirt is praised 

This is the actual review:
I liked this shirt so much i ordered a second one online in the same size but different color. unfortunately the second one is much shorter than the first and looks less like a tunic or more like a cropped flowy top - not a good look. so the second one is going back. otherwise the shirt would get 5 stars. washes well, super soft and comfy, and looks great with all kinds of bottoms...jeans, cords, leggings. wish it came in more colors. i typically wear a m or l in retailer tops and took the l in th

Personalized Message Output:
 ```json
{
  "personalized_message": "Thank you for your detailed feedback! We're glad to hear you love the softness and versatility of the shirt. We're sorry to learn about the sizing inconsistency with your second order. Your input is valuable 

This is the actual review:
I liked this shirt so much i ordered a second one online in the same size but different color. unfortunately the second one is much shorter than the first and looks less like a tunic or more like a cropped flowy top - not a good look. so the second one is going back. otherwise the shirt would get 5 stars. washes well, super soft and comfy, and looks great with all kinds of bottoms...jeans, cords, leggings. wish it came in more colors. i typically wear a m or l in retailer tops and took the l in th

Retail Insight Output:
 ```json
{
  "retail_insight": {
    "suggestions": [
      {
        "highlight_features": "Consider emphasizing the softness and stylish design in marketing materials, as these features resonate well with customers."
      },
      { 


----- Processing Review 2 (Original Index: 8421) -----
This is the actual review:
Saw this top on-line and thought i would give it a try. i am glad i did. there is not a thing wrong with this top. cute design, nice weight fabric,(no cami needed underneath) and it fits very well. for reference i am 5' 1", 120lbs. and 35c. and of course it goes with literally every piece of clothing you would wear on the bottom half of your body!
this is a keeper.

Categories Output:
 ```json
{
  "Categories": {
    "Division": "General",
    "Department": "Tops",
    "Class": "Blouses",
    "My Category": "Possible Female Shopper"
  },
  "Review": { 

This is the actual review:
Saw this top on-line and thought i would give it a try. i am glad i did. there is not a thing wrong with this top. cute design, nice weight fabric,(no cami needed underneath) and it fits very well. for reference i am 5' 1", 120lbs. and 35c. and of course it goes with literally every piece of clothing you would wear on the bottom half of your body!
this is a keeper.

Sentiment Output:
 ```json
{
  "sentiment": "positive"
}
``` 

This is the actual review:
Saw this top on-line and thought i would give it a try. i am glad i did. there is not a thing wrong with this top. cute design, nice weight fabric,(no cami needed underneath) and it fits very well. for reference i am 5' 1", 120lbs. and 35c. and of course it goes with literally every piece of clothing you would wear on the bottom half of your body!
this is a keeper.

Summary Output:
 ```json
{
  "summary": "The reviewer is highly satisfied with the blouse, praising its cute design, appropriate fabric weight, and perfect fit. They find it versatile enough to pair with various bottoms and consider it a keeper."
}
``` 

This is the actual review:
Saw this top on-line and thought i would give it a try. i am glad i did. there is not a thing wrong with this top. cute design, nice weight fabric,(no cami needed underneath) and it fits very well. for reference i am 5' 1", 120lbs. and 35c. and of course it goes with literally every piece of clothing you would wear on the bottom half of your body!
this is a keeper.

Personalized Message Output:
 ```json
{
  "personalized_message": "Thank you for your wonderful feedback! We're thrilled to hear that you love the design, fabric weight, and fit of your new blouse. It's great to know it pairs well with your wardrobe. Enjoy 

This is the actual review:
Saw this top on-line and thought i would give it a try. i am glad i did. there is not a thing wrong with this top. cute design, nice weight fabric,(no cami needed underneath) and it fits very well. for reference i am 5' 1", 120lbs. and 35c. and of course it goes with literally every piece of clothing you would wear on the bottom half of your body!
this is a keeper.

Retail Insight Output:
 ```json
{
  "retail_insight": {
    "suggestions": [
      "Highlight the softness and stylish design in marketing materials, as these features resonate well with customers.",
      "Showcase customer fit examples to help potential buyers make 


----- Processing Review 3 (Original Index: 12715) -----
This is the actual review:
I wanted to love this dress, the open back, the lightweight fabric, and fun print. perfect for spring and summer. it could be i'm too tiny for this dress, but the front of the dress wasn't flattering. it just hung and there was an odd tuck in the waist that made it look like it ballooned out under my chest above my waist.

Categories Output:
 ```json
{
  "Categories": {
    "Division": "General",
    "Department": "Dresses",
    "Class": "Dresses",
    "My Category": "Possible Female Shopper"
  },
  "Review": { 

This is the actual review:
I wanted to love this dress, the open back, the lightweight fabric, and fun print. perfect for spring and summer. it could be i'm too tiny for this dress, but the front of the dress wasn't flattering. it just hung and there was an odd tuck in the waist that made it look like it ballooned out under my chest above my waist.

Sentiment Output:
 ```json
{
  "sentiment": "neutral"
}
``` 

This is the actual review:
I wanted to love this dress, the open back, the lightweight fabric, and fun print. perfect for spring and summer. it could be i'm too tiny for this dress, but the front of the dress wasn't flattering. it just hung and there was an odd tuck in the waist that made it look like it ballooned out under my chest above my waist.

Summary Output:
 ```json
{
  "summary": "The reviewer expresses disappointment with the dress, citing an unflattering fit and an awkward tuck in the waist, despite appreciating its open back, lightweight fabric, and fun print."
}
``` 

This is the actual review:
I wanted to love this dress, the open back, the lightweight fabric, and fun print. perfect for spring and summer. it could be i'm too tiny for this dress, but the front of the dress wasn't flattering. it just hung and there was an odd tuck in the waist that made it look like it ballooned out under my chest above my waist.

Personalized Message Output:
 ```json
{
  "personalized_message": "Thank you for your feedback! We're sorry to hear that the fit of the dress didn't meet your expectations. We appreciate your insights on the design and will take them into consideration. We hope you find 

This is the actual review:
I wanted to love this dress, the open back, the lightweight fabric, and fun print. perfect for spring and summer. it could be i'm too tiny for this dress, but the front of the dress wasn't flattering. it just hung and there was an odd tuck in the waist that made it look like it ballooned out under my chest above my waist.

Retail Insight Output:
 ```json
{
  "retail_insight": {
    "suggestions": [
      {
        "highlight_features": "Emphasize the softness and stylish design of the dress in marketing materials, as these attributes resonate well with customers." 


----- Processing Review 4 (Original Index: 18326) -----
This is the actual review:
I'm sad i was unable to get an xs, as the small is somewhat big on my 125-lb 5'6 frame. i'm keeping it for the sale price though! when i put it on in the store i got so many compliments from other customers who went to find it! i think it is meant to run large, and honestly i still think even with it looking big it still looked better in person than the model. i would recommend ordering it if you can and don't mind pieces that drape like this!

Categories Output:
 ```json
{
  "Categories": {
    "Division": "General Petite",
    "Department": "Jackets",
    "Class": "Jackets",
    "My Category": "Possible Female Shopper"
  },
  "Review": { 

This is the actual review:
I'm sad i was unable to get an xs, as the small is somewhat big on my 125-lb 5'6 frame. i'm keeping it for the sale price though! when i put it on in the store i got so many compliments from other customers who went to find it! i think it is meant to run large, and honestly i still think even with it looking big it still looked better in person than the model. i would recommend ordering it if you can and don't mind pieces that drape like this!

Sentiment Output:
 ```json
{
  "sentiment": "positive"
}
``` 

This is the actual review:
I'm sad i was unable to get an xs, as the small is somewhat big on my 125-lb 5'6 frame. i'm keeping it for the sale price though! when i put it on in the store i got so many compliments from other customers who went to find it! i think it is meant to run large, and honestly i still think even with it looking big it still looked better in person than the model. i would recommend ordering it if you can and don't mind pieces that drape like this!

Summary Output:
 ```json
{
  "summary": "The reviewer expresses disappointment over the unavailability of an XS size, as the small is slightly large for their frame. However, they appreciate the pullover's style and received numerous compliments, suggesting it looks better 

This is the actual review:
I'm sad i was unable to get an xs, as the small is somewhat big on my 125-lb 5'6 frame. i'm keeping it for the sale price though! when i put it on in the store i got so many compliments from other customers who went to find it! i think it is meant to run large, and honestly i still think even with it looking big it still looked better in person than the model. i would recommend ordering it if you can and don't mind pieces that drape like this!

Personalized Message Output:
 ```json
{
  "personalized_message": "Thank you for sharing your experience! We're glad to hear that you received compliments and found the jacket to be stylish, even if the size wasn't perfect for you. We appreciate your recommendation and hope you 

This is the actual review:
I'm sad i was unable to get an xs, as the small is somewhat big on my 125-lb 5'6 frame. i'm keeping it for the sale price though! when i put it on in the store i got so many compliments from other customers who went to find it! i think it is meant to run large, and honestly i still think even with it looking big it still looked better in person than the model. i would recommend ordering it if you can and don't mind pieces that drape like this!

Retail Insight Output:
 ```json
{
  "retail_insight": {
    "suggestions": [
      {
        "highlight_features": "Consider emphasizing the softness and stylish design in marketing materials, as these features resonate well with customers."
      },
      { 


----- Processing Review 5 (Original Index: 17154) -----
This is the actual review:
Wow, this sweater is, as other reviewers have said, perfect weight and the colors are vibrant and will work well with fall colors! i'm 5'11" so i'm always hoping the length works and in this case it does! i am very happy with this!

Categories Output:
 ```json
{
  "Categories": {
    "Division": "General Petite",
    "Department": "Tops",
    "Class": "Sweaters",
    "My Category": "Possible Female Shopper"
  },
  "Review": { 

This is the actual review:
Wow, this sweater is, as other reviewers have said, perfect weight and the colors are vibrant and will work well with fall colors! i'm 5'11" so i'm always hoping the length works and in this case it does! i am very happy with this!

Sentiment Output:
 ```json
{
  "sentiment": "positive"
}
``` 

This is the actual review:
Wow, this sweater is, as other reviewers have said, perfect weight and the colors are vibrant and will work well with fall colors! i'm 5'11" so i'm always hoping the length works and in this case it does! i am very happy with this!

Summary Output:
 ```json
{
  "summary": "The reviewer is pleased with the sweater's perfect weight and vibrant colors, which complement fall styles. They also appreciate the length, finding it suitable for their height of 5'11\"."
}
``` 

This is the actual review:
Wow, this sweater is, as other reviewers have said, perfect weight and the colors are vibrant and will work well with fall colors! i'm 5'11" so i'm always hoping the length works and in this case it does! i am very happy with this!

Personalized Message Output:
 ```json
{
  "personalized_message": "Thank you for your wonderful feedback! We're thrilled to hear that you love the perfect weight and vibrant colors of your new sweater. It's great to know that the length works well for you at 5 

This is the actual review:
Wow, this sweater is, as other reviewers have said, perfect weight and the colors are vibrant and will work well with fall colors! i'm 5'11" so i'm always hoping the length works and in this case it does! i am very happy with this!

Retail Insight Output:
 ```json
{
  "retail_insight": {
    "suggestions": [
      {
        "highlight_features": "Consider highlighting the softness and stylish design in marketing materials, as these features resonate well with customers."
      },
      { 


----- Processing Review 6 (Original Index: 11257) -----
This is the actual review:
Love these jeans. fit great. i've had other colored denim from ag that stretched out too much, were too thin, but these are a nice thicker fabric. i ordered the red and the moss, and the red fit a little better than the moss in the same size. not sure if the dye process affects the fabric but the red pair seemed a bit more substantial. the moss is great too though, so i may keep both.

Categories Output:
 ```json
{
  "Categories": {
    "Division": "General",
    "Department": "Bottoms",
    "Class": "Jeans",
    "My Category": "Possible Female Shopper"
  },
  "Review": { 

This is the actual review:
Love these jeans. fit great. i've had other colored denim from ag that stretched out too much, were too thin, but these are a nice thicker fabric. i ordered the red and the moss, and the red fit a little better than the moss in the same size. not sure if the dye process affects the fabric but the red pair seemed a bit more substantial. the moss is great too though, so i may keep both.

Sentiment Output:
 ```json
{
  "sentiment": "positive",
  "review": {
    "division": "General",
    "department": "Bottoms",
    "class": "Jeans",
    "content": "Love these jeans. fit 

This is the actual review:
Love these jeans. fit great. i've had other colored denim from ag that stretched out too much, were too thin, but these are a nice thicker fabric. i ordered the red and the moss, and the red fit a little better than the moss in the same size. not sure if the dye process affects the fabric but the red pair seemed a bit more substantial. the moss is great too though, so i may keep both.

Summary Output:
 ```json
{
  "summary": "The reviewer loves the jeans for their great fit and thicker fabric, noting a slight difference in fit between the red and moss colors, with a preference for the red pair."
}
``` 

This is the actual review:
Love these jeans. fit great. i've had other colored denim from ag that stretched out too much, were too thin, but these are a nice thicker fabric. i ordered the red and the moss, and the red fit a little better than the moss in the same size. not sure if the dye process affects the fabric but the red pair seemed a bit more substantial. the moss is great too though, so i may keep both.

Personalized Message Output:
 ```json
{
  "personalized_message": "Thank you for your detailed feedback! We're delighted to hear that you love the fit and fabric of your new jeans. It's great to know that both the red and moss colors are working for you. 

This is the actual review:
Love these jeans. fit great. i've had other colored denim from ag that stretched out too much, were too thin, but these are a nice thicker fabric. i ordered the red and the moss, and the red fit a little better than the moss in the same size. not sure if the dye process affects the fabric but the red pair seemed a bit more substantial. the moss is great too though, so i may keep both.

Retail Insight Output:
 ```json
{
  "retail_insight": {
    "suggestions": [
      {
        "highlight_features": "Emphasize the softness and stylish design of the jeans in marketing materials, as these attributes resonate well with customers." 


----- Processing Review 7 (Original Index: 21704) -----
This is the actual review:
I bought the green pair in store and loved their fit. i ended up buying the rust color online too! i'm 5'3" 125 and i went down a size to a 26. cause they will stretch a little. perfect length for my height too great with wedges or boots.

Categories Output:
 ```json
{
  "Categories": {
    "Division": "General",
    "Department": "Bottoms",
    "Class": "Jeans",
    "My Category": "Possible Female Shopper"
  },
  "Review": { 

This is the actual review:
I bought the green pair in store and loved their fit. i ended up buying the rust color online too! i'm 5'3" 125 and i went down a size to a 26. cause they will stretch a little. perfect length for my height too great with wedges or boots.

Sentiment Output:
 ```json
{
  "sentiment": "positive"
}
``` 

This is the actual review:
I bought the green pair in store and loved their fit. i ended up buying the rust color online too! i'm 5'3" 125 and i went down a size to a 26. cause they will stretch a little. perfect length for my height too great with wedges or boots.

Summary Output:
 ```json
{
  "summary": "The reviewer is pleased with the fit and length of the jeans, noting that they went down a size for a better fit and found them perfect for their height, complementing their style with wedges or boots." 

This is the actual review:
I bought the green pair in store and loved their fit. i ended up buying the rust color online too! i'm 5'3" 125 and i went down a size to a 26. cause they will stretch a little. perfect length for my height too great with wedges or boots.

Personalized Message Output:
 ```json
{
  "personalized_message": "Thank you for your fantastic review! We're delighted to hear that you found the perfect fit and length in both colors of your jeans. Enjoy styling them with your favorite wedges and boots!"
}
``` 

This is the actual review:
I bought the green pair in store and loved their fit. i ended up buying the rust color online too! i'm 5'3" 125 and i went down a size to a 26. cause they will stretch a little. perfect length for my height too great with wedges or boots.

Retail Insight Output:
 ```json
{
  "retail_insight": {
    "suggestions": [
      {
        "highlight_features": "Consider highlighting the softness and stylish design in marketing materials, as these features resonate well with customers."
      },
      { 


----- Processing Review 8 (Original Index: 19529) -----
This is the actual review:
Cashmere is soft as a feather, yet warm, easy to wear and the pointelle stitches are lovely. i feel lovely wearing this and i'm really not that kind of "lady". so, if you imagine a side of yourself wanting to try out feeling lovely, bite the bullet and buy this cardigan. it does run small in my bust area, but all the rest fits well.

Categories Output:
 ```json
{
  "Categories": {
    "Division": "General Petite",
    "Department": "Tops",
    "Class": "Fine gauge",
    "My Category": "Possible Female Shopper"
  },
  "Review": { 

This is the actual review:
Cashmere is soft as a feather, yet warm, easy to wear and the pointelle stitches are lovely. i feel lovely wearing this and i'm really not that kind of "lady". so, if you imagine a side of yourself wanting to try out feeling lovely, bite the bullet and buy this cardigan. it does run small in my bust area, but all the rest fits well.

Sentiment Output:
 ```json
{
  "sentiment": "positive"
}
``` 

This is the actual review:
Cashmere is soft as a feather, yet warm, easy to wear and the pointelle stitches are lovely. i feel lovely wearing this and i'm really not that kind of "lady". so, if you imagine a side of yourself wanting to try out feeling lovely, bite the bullet and buy this cardigan. it does run small in my bust area, but all the rest fits well.

Summary Output:
 ```json
{
  "summary": "The reviewer finds the cashmere cardigan to be incredibly soft and warm, with beautiful pointelle stitches. They feel lovely wearing it, despite not typically identifying as a 'lady'. However, they note that it 

This is the actual review:
Cashmere is soft as a feather, yet warm, easy to wear and the pointelle stitches are lovely. i feel lovely wearing this and i'm really not that kind of "lady". so, if you imagine a side of yourself wanting to try out feeling lovely, bite the bullet and buy this cardigan. it does run small in my bust area, but all the rest fits well.

Personalized Message Output:
 ```json
{
  "personalized_message": "Thank you for your wonderful feedback! We're thrilled to hear that you love the softness and style of your new pullover. Enjoy wearing it!"
}
``` 

This is the actual review:
Cashmere is soft as a feather, yet warm, easy to wear and the pointelle stitches are lovely. i feel lovely wearing this and i'm really not that kind of "lady". so, if you imagine a side of yourself wanting to try out feeling lovely, bite the bullet and buy this cardigan. it does run small in my bust area, but all the rest fits well.

Retail Insight Output:
 ```json
{
  "retail_insight": {
    "suggestions": [
      {
        "highlight_features": "Consider emphasizing the softness and stylish design of the cashmere cardigan in marketing materials, as these attributes resonate well with customers." 


----- Processing Review 9 (Original Index: 18440) -----
This is the actual review:
First of all, i'm not a "navy" person at all. i much prefer black or other bright colors. i received an e'mail showing this trench last saturday and i pulled it up online and noticed how darling it looked. i didn't order it, but i went back the following day to check it out again. it was nowhere on the website. then a few days ago, back it popped up online again. i noticed it wasn't in my local store so i thought i'd go in and look this weekend. i just had to see it. i actually made it to the st

Categories Output:
 ```json
{
  "Categories": {
    "Division": "General",
    "Department": "Jackets",
    "Class": "Outerwear",
    "My Category": "Possible Female Shopper"
  },
  "Review": { 

This is the actual review:
First of all, i'm not a "navy" person at all. i much prefer black or other bright colors. i received an e'mail showing this trench last saturday and i pulled it up online and noticed how darling it looked. i didn't order it, but i went back the following day to check it out again. it was nowhere on the website. then a few days ago, back it popped up online again. i noticed it wasn't in my local store so i thought i'd go in and look this weekend. i just had to see it. i actually made it to the st

Sentiment Output:
 ```json
{
  "sentiment": "positive"
}
``` 

This is the actual review:
First of all, i'm not a "navy" person at all. i much prefer black or other bright colors. i received an e'mail showing this trench last saturday and i pulled it up online and noticed how darling it looked. i didn't order it, but i went back the following day to check it out again. it was nowhere on the website. then a few days ago, back it popped up online again. i noticed it wasn't in my local store so i thought i'd go in and look this weekend. i just had to see it. i actually made it to the st

Summary Output:
 ```json
{
  "summary": "The reviewer initially hesitated due to a preference for colors other than navy but found the trench coat appealing after seeing it online. They expressed a desire to see it in person despite it not being available in their 

This is the actual review:
First of all, i'm not a "navy" person at all. i much prefer black or other bright colors. i received an e'mail showing this trench last saturday and i pulled it up online and noticed how darling it looked. i didn't order it, but i went back the following day to check it out again. it was nowhere on the website. then a few days ago, back it popped up online again. i noticed it wasn't in my local store so i thought i'd go in and look this weekend. i just had to see it. i actually made it to the st

Personalized Message Output:
 ```json
{
  "personalized_message": "Thank you for sharing your experience! We're glad to hear that you found the trench appealing enough to seek it out, even if navy isn't your usual color. We hope you enjoy your visit to the 

This is the actual review:
First of all, i'm not a "navy" person at all. i much prefer black or other bright colors. i received an e'mail showing this trench last saturday and i pulled it up online and noticed how darling it looked. i didn't order it, but i went back the following day to check it out again. it was nowhere on the website. then a few days ago, back it popped up online again. i noticed it wasn't in my local store so i thought i'd go in and look this weekend. i just had to see it. i actually made it to the st

Retail Insight Output:
 ```json
{
  "retail_insight": {
    "suggestions": [
      {
        "highlight_features": "Consider emphasizing the softness and stylish design of the jackets in marketing materials, as these attributes resonate well with customers."
      }, 


----- Processing Review 10 (Original Index: 854) -----
This is the actual review:
I did not realize that this knitted vest had an attached gauzy under-layer. i can see it now in the photo, but i just thought it wasn't part of the actual vest. when the vest arrived i realized sadly that this flowing under-layer was part of the design. i don't know why because the yarn and vest are lovely without it. in my opinion, the under-layer takes away from the beautiful vest. i guess i could have spent some time and removed it, but at that price i just sent it back. too bad because the m

Categories Output:
 ```json
{
  "Categories": {
    "Division": "General Petite",
    "Department": "Tops",
    "Class": "Sweaters",
    "My Category": "Possible Female Shopper"
  },
  "Review": { 

This is the actual review:
I did not realize that this knitted vest had an attached gauzy under-layer. i can see it now in the photo, but i just thought it wasn't part of the actual vest. when the vest arrived i realized sadly that this flowing under-layer was part of the design. i don't know why because the yarn and vest are lovely without it. in my opinion, the under-layer takes away from the beautiful vest. i guess i could have spent some time and removed it, but at that price i just sent it back. too bad because the m

Sentiment Output:
 ```json
{
  "sentiment": "negative"
}
``` 

This is the actual review:
I did not realize that this knitted vest had an attached gauzy under-layer. i can see it now in the photo, but i just thought it wasn't part of the actual vest. when the vest arrived i realized sadly that this flowing under-layer was part of the design. i don't know why because the yarn and vest are lovely without it. in my opinion, the under-layer takes away from the beautiful vest. i guess i could have spent some time and removed it, but at that price i just sent it back. too bad because the m

Summary Output:
 ```json
{
  "summary": "The reviewer was disappointed to discover that the knitted vest included an attached gauzy under-layer, which they felt detracted from the overall beauty of the vest. Despite appreciating the yarn and design, they chose to 

This is the actual review:
I did not realize that this knitted vest had an attached gauzy under-layer. i can see it now in the photo, but i just thought it wasn't part of the actual vest. when the vest arrived i realized sadly that this flowing under-layer was part of the design. i don't know why because the yarn and vest are lovely without it. in my opinion, the under-layer takes away from the beautiful vest. i guess i could have spent some time and removed it, but at that price i just sent it back. too bad because the m

Personalized Message Output:
 ```json
{
  "personalized_message": "Thank you for your feedback! We're sorry to hear that the attached gauzy under-layer of the knitted vest did not meet your expectations. We appreciate your insights and will take them into consideration for future 

This is the actual review:
I did not realize that this knitted vest had an attached gauzy under-layer. i can see it now in the photo, but i just thought it wasn't part of the actual vest. when the vest arrived i realized sadly that this flowing under-layer was part of the design. i don't know why because the yarn and vest are lovely without it. in my opinion, the under-layer takes away from the beautiful vest. i guess i could have spent some time and removed it, but at that price i just sent it back. too bad because the m

Retail Insight Output:
 ```json
{
  "retail_insight": {
    "suggestions": [
      {
        "highlight_features": "Consider emphasizing the softness and stylish design of the knitted vest in marketing materials, as these attributes are likely to resonate well with 


----- Processing Review 11 (Original Index: 3107) -----
This is the actual review:
I liked this so much that i bought it in all three colors, but send berry color back. the top is flattering and looks good. i did wash it at home, it seems to shrink a bit. but i ordered up for longer length. i really like the style. it is perfect to wear by itself or as a layer. perfect for fall and winter.

Categories Output:
 ```json
{
  "Categories": {
    "Division": "General Petite",
    "Department": "Tops",
    "Class": "Knits",
    "My Category": "Possible Female Shopper"
  },
  "Review": { 

This is the actual review:
I liked this so much that i bought it in all three colors, but send berry color back. the top is flattering and looks good. i did wash it at home, it seems to shrink a bit. but i ordered up for longer length. i really like the style. it is perfect to wear by itself or as a layer. perfect for fall and winter.

Sentiment Output:
 ```json
{
  "sentiment": "positive"
}
``` 

This is the actual review:
I liked this so much that i bought it in all three colors, but send berry color back. the top is flattering and looks good. i did wash it at home, it seems to shrink a bit. but i ordered up for longer length. i really like the style. it is perfect to wear by itself or as a layer. perfect for fall and winter.

Summary Output:
 ```json
{
  "summary": "The reviewer loves the flattering style of the pullover, purchased it in multiple colors, and finds it versatile for layering. However, they noted slight shrinkage after washing."
}
``` 

This is the actual review:
I liked this so much that i bought it in all three colors, but send berry color back. the top is flattering and looks good. i did wash it at home, it seems to shrink a bit. but i ordered up for longer length. i really like the style. it is perfect to wear by itself or as a layer. perfect for fall and winter.

Personalized Message Output:
 ```json
{
  "personalized_message": "Thank you for your wonderful feedback! We're thrilled to hear that you love the softness and style of your new pullover. Enjoy wearing it!"
}
``` 

This is the actual review:
I liked this so much that i bought it in all three colors, but send berry color back. the top is flattering and looks good. i did wash it at home, it seems to shrink a bit. but i ordered up for longer length. i really like the style. it is perfect to wear by itself or as a layer. perfect for fall and winter.

Retail Insight Output:
 ```json
{
  "retail_insight": {
    "suggestions": [
      {
        "highlight_features": "Consider emphasizing the softness and stylish design in marketing materials, as these features resonate well with customers."
      },
      { 


----- Processing Review 12 (Original Index: 5391) -----
This is the actual review:
This duster is a nice light weight dark navy that you can throw on to add a little something to a your outfit without adding bulk or warmth. looks nice over dress, jeans, crops. just a fun little item to have in you closet that you can grab to put a little twist to an outfit. best part of the duster....it has pockets!! i love being able to slide my iphone into a pocket while on the go!

Categories Output:
 ```json
{
  "Categories": {
    "Division": "General",
    "Department": "Tops",
    "Class": "Knits",
    "My Category": "Possible Female Shopper"
  },
  "Review": { 

This is the actual review:
This duster is a nice light weight dark navy that you can throw on to add a little something to a your outfit without adding bulk or warmth. looks nice over dress, jeans, crops. just a fun little item to have in you closet that you can grab to put a little twist to an outfit. best part of the duster....it has pockets!! i love being able to slide my iphone into a pocket while on the go!

Sentiment Output:
 ```json
{
  "sentiment": "positive"
}
``` 

This is the actual review:
This duster is a nice light weight dark navy that you can throw on to add a little something to a your outfit without adding bulk or warmth. looks nice over dress, jeans, crops. just a fun little item to have in you closet that you can grab to put a little twist to an outfit. best part of the duster....it has pockets!! i love being able to slide my iphone into a pocket while on the go!

Summary Output:
 ```json
{
  "summary": "The reviewer finds the duster to be a lightweight and stylish addition to outfits, appreciating its versatility and the convenience of pockets."
}
``` 

This is the actual review:
This duster is a nice light weight dark navy that you can throw on to add a little something to a your outfit without adding bulk or warmth. looks nice over dress, jeans, crops. just a fun little item to have in you closet that you can grab to put a little twist to an outfit. best part of the duster....it has pockets!! i love being able to slide my iphone into a pocket while on the go!

Personalized Message Output:
 ```json
{
  "personalized_message": "Thank you for your fantastic review! We're delighted to hear that you appreciate the lightweight design and versatility of the duster. It's great to know that the pockets are a hit for your on-the-go 

This is the actual review:
This duster is a nice light weight dark navy that you can throw on to add a little something to a your outfit without adding bulk or warmth. looks nice over dress, jeans, crops. just a fun little item to have in you closet that you can grab to put a little twist to an outfit. best part of the duster....it has pockets!! i love being able to slide my iphone into a pocket while on the go!

Retail Insight Output:
 ```json
{
  "retail_insight": {
    "suggestions": [
      {
        "feature": "Softness",
        "action": "Highlight the softness of the fabric in marketing materials, as it enhances comfort and appeal." 


----- Processing Review 13 (Original Index: 4364) -----
This is the actual review:
This top fits well as expected for the size. i love the ease of the top and wear it both casually and a bit more dressed up. great styling on the back.

Categories Output:
 ```json
{
  "Categories": {
    "Division": "General",
    "Department": "Tops",
    "Class": "Blouses",
    "My Category": "Possible Female Shopper"
  },
  "Review": { 

This is the actual review:
This top fits well as expected for the size. i love the ease of the top and wear it both casually and a bit more dressed up. great styling on the back.

Sentiment Output:
 ```json
{
  "sentiment": "positive"
}
``` 

This is the actual review:
This top fits well as expected for the size. i love the ease of the top and wear it both casually and a bit more dressed up. great styling on the back.

Summary Output:
 ```json
{
  "summary": "The reviewer finds the blouse fits well and appreciates its versatility for both casual and dressed-up occasions, highlighting the great styling on the back."
}
``` 

This is the actual review:
This top fits well as expected for the size. i love the ease of the top and wear it both casually and a bit more dressed up. great styling on the back.

Personalized Message Output:
 ```json
{
  "personalized_message": "Thank you for your wonderful feedback! We're thrilled to hear that you love the fit and versatility of your new top. Enjoy styling it for both casual and dressed-up occasions!"
}
``` 

This is the actual review:
This top fits well as expected for the size. i love the ease of the top and wear it both casually and a bit more dressed up. great styling on the back.

Retail Insight Output:
 ```json
{
  "retail_insight": {
    "suggestions": [
      {
        "feature": "softness",
        "action": "Highlight the softness of the fabric in marketing materials."
      },
      {
        "feature 


----- Processing Review 14 (Original Index: 20934) -----
This is the actual review:
I bought this in coral, it is soft and the fit is flattering. the perfect top for transitioning into fall here in the desert.

Categories Output:
 ```json
{
  "Categories": {
    "Division": "General",
    "Department": "Tops",
    "Class": "Knits",
    "My Category": "Possible Female Shopper"
  },
  "Review": { 

This is the actual review:
I bought this in coral, it is soft and the fit is flattering. the perfect top for transitioning into fall here in the desert.

Sentiment Output:
 ```json
{
  "sentiment": "positive"
}
``` 

This is the actual review:
I bought this in coral, it is soft and the fit is flattering. the perfect top for transitioning into fall here in the desert.

Summary Output:
 ```json
{
  "summary": "The reviewer praises the pullover for its softness and flattering fit, highlighting its suitability for transitioning into fall."
}
``` 

This is the actual review:
I bought this in coral, it is soft and the fit is flattering. the perfect top for transitioning into fall here in the desert.

Personalized Message Output:
 ```json
{
  "personalized_message": "Thank you for your wonderful feedback! We're thrilled to hear that you love the softness and style of your new pullover. Enjoy wearing it!"
}
``` 

This is the actual review:
I bought this in coral, it is soft and the fit is flattering. the perfect top for transitioning into fall here in the desert.

Retail Insight Output:
 ```json
{
  "retail_insight": {
    "suggestions": [
      {
        "highlight_features": "Emphasize the softness and stylish design in marketing materials, as these features resonate well with customers."
      },
      { 


----- Processing Review 15 (Original Index: 558) -----
This is the actual review:
Love this dress! just wore it last night to an early valentine's dinner. very classy, simple, but gorgeous. i'm a size 2 in just about everything but i always buy a size 4 in retailer dresses because i'm tall (5'9") and have broad shoulders and a long torso, and sizing up in dresses works better all around. i am also not busty (32b). i did get this in a 4. this dress was a little awkward at first trying to hook and then zip but once i got that down it worked. the hook is right up under your

Categories Output:
 ```json
{
  "Categories": {
    "Division": "General",
    "Department": "Dresses",
    "Class": "Dresses",
    "My Category": "Possible Female Shopper"
  },
  "Review": { 

This is the actual review:
Love this dress! just wore it last night to an early valentine's dinner. very classy, simple, but gorgeous. i'm a size 2 in just about everything but i always buy a size 4 in retailer dresses because i'm tall (5'9") and have broad shoulders and a long torso, and sizing up in dresses works better all around. i am also not busty (32b). i did get this in a 4. this dress was a little awkward at first trying to hook and then zip but once i got that down it worked. the hook is right up under your

Sentiment Output:
 ```json
{
  "sentiment": "positive"
}
``` 

This is the actual review:
Love this dress! just wore it last night to an early valentine's dinner. very classy, simple, but gorgeous. i'm a size 2 in just about everything but i always buy a size 4 in retailer dresses because i'm tall (5'9") and have broad shoulders and a long torso, and sizing up in dresses works better all around. i am also not busty (32b). i did get this in a 4. this dress was a little awkward at first trying to hook and then zip but once i got that down it worked. the hook is right up under your

Summary Output:
 ```json
{
  "summary": "The reviewer loves the dress for its classy and simple design, noting that it fits well after adjusting the hook and zip. They recommend sizing up for taller individuals."
}
``` 

This is the actual review:
Love this dress! just wore it last night to an early valentine's dinner. very classy, simple, but gorgeous. i'm a size 2 in just about everything but i always buy a size 4 in retailer dresses because i'm tall (5'9") and have broad shoulders and a long torso, and sizing up in dresses works better all around. i am also not busty (32b). i did get this in a 4. this dress was a little awkward at first trying to hook and then zip but once i got that down it worked. the hook is right up under your

Personalized Message Output:
 ```json
{
  "personalized_message": "Thank you for sharing your experience! We're delighted to hear that you found the dress classy and gorgeous for your Valentine's dinner. It's great to know that sizing up worked well for your tall frame. We 

This is the actual review:
Love this dress! just wore it last night to an early valentine's dinner. very classy, simple, but gorgeous. i'm a size 2 in just about everything but i always buy a size 4 in retailer dresses because i'm tall (5'9") and have broad shoulders and a long torso, and sizing up in dresses works better all around. i am also not busty (32b). i did get this in a 4. this dress was a little awkward at first trying to hook and then zip but once i got that down it worked. the hook is right up under your

Retail Insight Output:
 ```json
{
  "retail_insight": {
    "suggestions": [
      {
        "highlight_features": "Consider highlighting the softness and stylish design in marketing materials, as these features resonate well with customers."
      },
      { 


----- Processing Review 16 (Original Index: 3481) -----
This is the actual review:
I liked the concept of this vest, but when i tried it on, i felt like i was 9 years old again and wearing my brownies girl scout uniform. it was also very boxy and wide. it seemed to be well-made, though, and maybe would have worked if it were a different color.

Categories Output:
 ```json
{
  "Categories": {
    "Division": "General",
    "Department": "Tops",
    "Class": "Sweaters",
    "My Category": "Possible Female Shopper"
  },
  "Review": { 

This is the actual review:
I liked the concept of this vest, but when i tried it on, i felt like i was 9 years old again and wearing my brownies girl scout uniform. it was also very boxy and wide. it seemed to be well-made, though, and maybe would have worked if it were a different color.

Sentiment Output:
 ```json
{
  "sentiment": "neutral"
}
``` 

This is the actual review:
I liked the concept of this vest, but when i tried it on, i felt like i was 9 years old again and wearing my brownies girl scout uniform. it was also very boxy and wide. it seemed to be well-made, though, and maybe would have worked if it were a different color.

Summary Output:
 ```json
{
  "summary": "The reviewer finds the vest's concept appealing but feels it has a childish appearance and is too boxy. They acknowledge its good quality but suggest a different color might improve its appeal."
}
``` 

This is the actual review:
I liked the concept of this vest, but when i tried it on, i felt like i was 9 years old again and wearing my brownies girl scout uniform. it was also very boxy and wide. it seemed to be well-made, though, and maybe would have worked if it were a different color.

Personalized Message Output:
 ```json
{
  "personalized_message": "Thank you for your feedback! We're sorry to hear that the vest didn't meet your expectations in terms of fit and style. We appreciate your comments on the quality and will take them into consideration for future 

This is the actual review:
I liked the concept of this vest, but when i tried it on, i felt like i was 9 years old again and wearing my brownies girl scout uniform. it was also very boxy and wide. it seemed to be well-made, though, and maybe would have worked if it were a different color.

Retail Insight Output:
 ```json
{
  "retail_insight": {
    "suggestions": [
      {
        "highlight_features": "Consider emphasizing the softness and stylish design in marketing materials, as these features resonate well with customers."
      },
      { 


----- Processing Review 17 (Original Index: 15804) -----
This is the actual review:
This is a beautiful blouse and well made!!! i read the previous reviews and i'm usually between sizes 8 and 10, so i sized-up, and ordered size 12 and it fits well. i purchased the pink, and it's soo pretty! i'm very satisfied with my purchase, and i highly recommend this blouse.

Categories Output:
 ```json
{
  "Categories": {
    "Division": "General Petite",
    "Department": "Tops",
    "Class": "Blouses",
    "My Category": "Possible Female Shopper"
  },
  "Review": { 

This is the actual review:
This is a beautiful blouse and well made!!! i read the previous reviews and i'm usually between sizes 8 and 10, so i sized-up, and ordered size 12 and it fits well. i purchased the pink, and it's soo pretty! i'm very satisfied with my purchase, and i highly recommend this blouse.

Sentiment Output:
 ```json
{
  "sentiment": "positive"
}
``` 

This is the actual review:
This is a beautiful blouse and well made!!! i read the previous reviews and i'm usually between sizes 8 and 10, so i sized-up, and ordered size 12 and it fits well. i purchased the pink, and it's soo pretty! i'm very satisfied with my purchase, and i highly recommend this blouse.

Summary Output:
 ```json
{
  "summary": "The reviewer finds the blouse beautiful and well-made, appreciates the fit after sizing up, and is very satisfied with the pink color, recommending it highly."
}
``` 

This is the actual review:
This is a beautiful blouse and well made!!! i read the previous reviews and i'm usually between sizes 8 and 10, so i sized-up, and ordered size 12 and it fits well. i purchased the pink, and it's soo pretty! i'm very satisfied with my purchase, and i highly recommend this blouse.

Personalized Message Output:
 ```json
{
  "personalized_message": "Thank you for your wonderful feedback! We're thrilled to hear that you find the blouse beautiful and well-made. It's great to know that sizing up worked perfectly for you. Enjoy wearing your pretty pink blouse 

This is the actual review:
This is a beautiful blouse and well made!!! i read the previous reviews and i'm usually between sizes 8 and 10, so i sized-up, and ordered size 12 and it fits well. i purchased the pink, and it's soo pretty! i'm very satisfied with my purchase, and i highly recommend this blouse.

Retail Insight Output:
 ```json
{
  "retail_insight": {
    "suggestions": [
      {
        "feature": "softness",
        "action": "Highlight the softness of the fabric in marketing materials to attract customers who prioritize comfort." 


----- Processing Review 18 (Original Index: 8649) -----
This is the actual review:
I wanted to love this top but couldn't. i am curvy (36/27/39; 32dd) and this accentuated my hips (which is not needed) and made my chest/whole shape seem boxy. i could only recommend this top to someone who is smaller chested with a straight build- the bottom would help give some shape; pleating on front would help chest seem larger. i did love the color and construction, lace details, 3/4 sleeves.

Categories Output:
 ```json
{
  "Categories": {
    "Division": "General",
    "Department": "Tops",
    "Class": "Blouses",
    "My Category": "Possible Female Shopper"
  },
  "Review": { 

This is the actual review:
I wanted to love this top but couldn't. i am curvy (36/27/39; 32dd) and this accentuated my hips (which is not needed) and made my chest/whole shape seem boxy. i could only recommend this top to someone who is smaller chested with a straight build- the bottom would help give some shape; pleating on front would help chest seem larger. i did love the color and construction, lace details, 3/4 sleeves.

Sentiment Output:
 ```json
{
  "sentiment": "neutral"
}
``` 

This is the actual review:
I wanted to love this top but couldn't. i am curvy (36/27/39; 32dd) and this accentuated my hips (which is not needed) and made my chest/whole shape seem boxy. i could only recommend this top to someone who is smaller chested with a straight build- the bottom would help give some shape; pleating on front would help chest seem larger. i did love the color and construction, lace details, 3/4 sleeves.

Summary Output:
 ```json
{
  "summary": "The reviewer expresses disappointment with the fit of the blouse, stating it accentuates hips and creates a boxy appearance for curvier figures. However, they appreciate the color, construction, lace details, and 

This is the actual review:
I wanted to love this top but couldn't. i am curvy (36/27/39; 32dd) and this accentuated my hips (which is not needed) and made my chest/whole shape seem boxy. i could only recommend this top to someone who is smaller chested with a straight build- the bottom would help give some shape; pleating on front would help chest seem larger. i did love the color and construction, lace details, 3/4 sleeves.

Personalized Message Output:
 ```json
{
  "personalized_message": "Thank you for your honest feedback! We're sorry to hear that the fit of the top didn't work for your body shape. We appreciate your suggestions regarding pleating and will take them into consideration. We're 

This is the actual review:
I wanted to love this top but couldn't. i am curvy (36/27/39; 32dd) and this accentuated my hips (which is not needed) and made my chest/whole shape seem boxy. i could only recommend this top to someone who is smaller chested with a straight build- the bottom would help give some shape; pleating on front would help chest seem larger. i did love the color and construction, lace details, 3/4 sleeves.

Retail Insight Output:
 ```json
{
  "retail_insight": {
    "suggestions": [
      {
        "highlight_features": "Consider emphasizing the softness and stylish design of the blouses in marketing materials, as these attributes resonate well with customers." 


----- Processing Review 19 (Original Index: 11816) -----
This is the actual review:
The fit on this shirt is bizarre--it very much resembles a nursing top (but wouldn't actually work for that purpose). the side visible in the picture is nicely draped, but the other side can flap open and is completely different. i tried two different sizes and both were equally unfortunate!

Categories Output:
 ```json
{
  "Categories": {
    "Division": "General",
    "Department": "Tops",
    "Class": "Knits",
    "My Category": "Possible Female Shopper"
  },
  "Review": { 

This is the actual review:
The fit on this shirt is bizarre--it very much resembles a nursing top (but wouldn't actually work for that purpose). the side visible in the picture is nicely draped, but the other side can flap open and is completely different. i tried two different sizes and both were equally unfortunate!

Sentiment Output:
 ```json
{
  "sentiment": "negative"
}
``` 

This is the actual review:
The fit on this shirt is bizarre--it very much resembles a nursing top (but wouldn't actually work for that purpose). the side visible in the picture is nicely draped, but the other side can flap open and is completely different. i tried two different sizes and both were equally unfortunate!

Summary Output:
 ```json
{
  "summary": "The reviewer finds the fit of the shirt to be bizarre, comparing it to a nursing top that is not functional for that purpose. They note that while one side drapes nicely, the other side flaps 

This is the actual review:
The fit on this shirt is bizarre--it very much resembles a nursing top (but wouldn't actually work for that purpose). the side visible in the picture is nicely draped, but the other side can flap open and is completely different. i tried two different sizes and both were equally unfortunate!

Personalized Message Output:
 ```json
{
  "personalized_message": "Thank you for your feedback! We're sorry to hear that the fit of the shirt did not meet your expectations. We appreciate your input and will take it into consideration for future designs. If you have 

This is the actual review:
The fit on this shirt is bizarre--it very much resembles a nursing top (but wouldn't actually work for that purpose). the side visible in the picture is nicely draped, but the other side can flap open and is completely different. i tried two different sizes and both were equally unfortunate!

Retail Insight Output:
 ```json
{
  "retail_insight": {
    "suggestions": [
      "Highlight the softness and stylish design in marketing materials, as these features resonate well with customers.",
      "Showcase customer fit examples to help potential buyers make 


----- Processing Review 20 (Original Index: 9009) -----
This is the actual review:
Sooo disappointed with how flimsy this fabric is. i think this is the thinnest of all clothing items i have ever come across anywhere in my life. this is really a shame because the colors are stunning and it is a nice shirt style. another con is if you have any bit of back fat it shows every lump and bump . i do not recommend unless you have a lean back and the cost was $5.00!!!

Categories Output:
 ```json
{
  "Categories": {
    "Division": "General",
    "Department": "Tops",
    "Class": "Knits",
    "My Category": "Possible Female Shopper"
  },
  "Review": { 

This is the actual review:
Sooo disappointed with how flimsy this fabric is. i think this is the thinnest of all clothing items i have ever come across anywhere in my life. this is really a shame because the colors are stunning and it is a nice shirt style. another con is if you have any bit of back fat it shows every lump and bump . i do not recommend unless you have a lean back and the cost was $5.00!!!

Sentiment Output:
 ```json
{
  "sentiment": "negative",
  "review": {
    "division": "General",
    "department": "Tops",
    "class": "Knits",
    "content": "Sooo disappointed with how 

This is the actual review:
Sooo disappointed with how flimsy this fabric is. i think this is the thinnest of all clothing items i have ever come across anywhere in my life. this is really a shame because the colors are stunning and it is a nice shirt style. another con is if you have any bit of back fat it shows every lump and bump . i do not recommend unless you have a lean back and the cost was $5.00!!!

Summary Output:
 ```json
{
  "summary": "The reviewer expresses disappointment with the flimsy fabric of the pullover, describing it as the thinnest clothing item they've encountered. While they appreciate the stunning colors and nice shirt style, they caution that it 

This is the actual review:
Sooo disappointed with how flimsy this fabric is. i think this is the thinnest of all clothing items i have ever come across anywhere in my life. this is really a shame because the colors are stunning and it is a nice shirt style. another con is if you have any bit of back fat it shows every lump and bump . i do not recommend unless you have a lean back and the cost was $5.00!!!

Personalized Message Output:
 ```json
{
  "personalized_message": "Thank you for your feedback. We're sorry to hear that the fabric did not meet your expectations and that you found it too thin. We appreciate your comments on the colors and style, and we will 

This is the actual review:
Sooo disappointed with how flimsy this fabric is. i think this is the thinnest of all clothing items i have ever come across anywhere in my life. this is really a shame because the colors are stunning and it is a nice shirt style. another con is if you have any bit of back fat it shows every lump and bump . i do not recommend unless you have a lean back and the cost was $5.00!!!

Retail Insight Output:
 ```json
{
  "retail_insight": {
    "suggestions": [
      {
        "highlight_features": "Consider emphasizing the softness and stylish design in marketing materials, as these features resonate well with customers."
      },
      { 


----- Processing Review 21 (Original Index: 1744) -----
This is the actual review:
Right out of the bag, this top didn't really match the one pictured here (it is the right top, though). the fabric was heavy and felt "damp" -- the way something feels coming out of the wash. very odd.
and the fit is nothing like what's shown on the model. the "v" is not fitted or nicely wrapped (or even tacked in place) -- it was just two loose, droopy pieces so most of my bra was showing no matter how many times i put the pieces back where they belonged (i'm neither "flat chested" nor "busty"

Categories Output:
 ```json
{
  "Categories": {
    "Division": "General",
    "Department": "Tops",
    "Class": "Knits",
    "My Category": "Possible Female Shopper"
  },
  "Review": { 

This is the actual review:
Right out of the bag, this top didn't really match the one pictured here (it is the right top, though). the fabric was heavy and felt "damp" -- the way something feels coming out of the wash. very odd.
and the fit is nothing like what's shown on the model. the "v" is not fitted or nicely wrapped (or even tacked in place) -- it was just two loose, droopy pieces so most of my bra was showing no matter how many times i put the pieces back where they belonged (i'm neither "flat chested" nor "busty"

Sentiment Output:
 ```json
{
  "sentiment": "negative"
}
``` 

This is the actual review:
Right out of the bag, this top didn't really match the one pictured here (it is the right top, though). the fabric was heavy and felt "damp" -- the way something feels coming out of the wash. very odd.
and the fit is nothing like what's shown on the model. the "v" is not fitted or nicely wrapped (or even tacked in place) -- it was just two loose, droopy pieces so most of my bra was showing no matter how many times i put the pieces back where they belonged (i'm neither "flat chested" nor "busty"

Summary Output:
 ```json
{
  "summary": "The reviewer expresses disappointment with the pullover, highlighting discrepancies in appearance compared to the product image, heavy fabric that feels damp, and an ill-fitting design that fails to hold its shape, resulting in an un 

This is the actual review:
Right out of the bag, this top didn't really match the one pictured here (it is the right top, though). the fabric was heavy and felt "damp" -- the way something feels coming out of the wash. very odd.
and the fit is nothing like what's shown on the model. the "v" is not fitted or nicely wrapped (or even tacked in place) -- it was just two loose, droopy pieces so most of my bra was showing no matter how many times i put the pieces back where they belonged (i'm neither "flat chested" nor "busty"

Personalized Message Output:
 ```json
{
  "personalized_message": "Thank you for your feedback. We're sorry to hear that the top did not meet your expectations in terms of fabric feel and fit. Your comments about the design and fit are valuable to us, and 

This is the actual review:
Right out of the bag, this top didn't really match the one pictured here (it is the right top, though). the fabric was heavy and felt "damp" -- the way something feels coming out of the wash. very odd.
and the fit is nothing like what's shown on the model. the "v" is not fitted or nicely wrapped (or even tacked in place) -- it was just two loose, droopy pieces so most of my bra was showing no matter how many times i put the pieces back where they belonged (i'm neither "flat chested" nor "busty"

Retail Insight Output:
 ```json
{
  "retail_insight": {
    "suggestions": [
      {
        "highlight_features": "Emphasize the softness and stylish design of the tops in marketing materials, as these attributes are important to customers." 


----- Processing Review 22 (Original Index: 10839) -----
This is the actual review:
I ordered both the green and the pink and needed the extra extra small petite in the shirt to make it work for me 
 nice light material cute fit 
 unfortunately didn't like the pink color was too bright for me  ( and although i ordered an extra extra small petite they sent me an extra small petite which is just slightly too big )and saw the mango in the store which i also didn't like the color

Categories Output:
 ```json
{
  "Categories": {
    "Division": "General",
    "Department": "Tops",
    "Class": "Knits",
    "My Category": "Possible Female Shopper"
  },
  "Review": { 

This is the actual review:
I ordered both the green and the pink and needed the extra extra small petite in the shirt to make it work for me 
 nice light material cute fit 
 unfortunately didn't like the pink color was too bright for me  ( and although i ordered an extra extra small petite they sent me an extra small petite which is just slightly too big )and saw the mango in the store which i also didn't like the color

Sentiment Output:
 ```json
{
  "sentiment": "positive"
}
``` 

This is the actual review:
I ordered both the green and the pink and needed the extra extra small petite in the shirt to make it work for me 
 nice light material cute fit 
 unfortunately didn't like the pink color was too bright for me  ( and although i ordered an extra extra small petite they sent me an extra small petite which is just slightly too big )and saw the mango in the store which i also didn't like the color

Summary Output:
 ```json
{
  "summary": "The reviewer ordered two colors of the pullover but found the pink too bright and received the wrong size, which was slightly too big. They appreciated the light material and cute fit but were disappointed with the color 

This is the actual review:
I ordered both the green and the pink and needed the extra extra small petite in the shirt to make it work for me 
 nice light material cute fit 
 unfortunately didn't like the pink color was too bright for me  ( and although i ordered an extra extra small petite they sent me an extra small petite which is just slightly too big )and saw the mango in the store which i also didn't like the color

Personalized Message Output:
 ```json
{
  "personalized_message": "Thank you for your feedback! We're glad to hear you liked the light material and cute fit of the shirts. We apologize for the mix-up with the sizing and the color not meeting your expectations. 

This is the actual review:
I ordered both the green and the pink and needed the extra extra small petite in the shirt to make it work for me 
 nice light material cute fit 
 unfortunately didn't like the pink color was too bright for me  ( and although i ordered an extra extra small petite they sent me an extra small petite which is just slightly too big )and saw the mango in the store which i also didn't like the color

Retail Insight Output:
 ```json
{
  "retail_insight": {
    "suggestions": [
      {
        "highlight_features": "Consider highlighting the softness and stylish design in marketing materials, as these features resonate well with customers."
      },
      { 


----- Processing Review 23 (Original Index: 3425) -----
This is the actual review:
I saw this dress in the new arrivals in red, and it caught my attention, but not enough to order it (the pictures online for other colors didn't look that great). well, i saw the black in the store and decided to try it on as it looked like hte cut would be cute. it fit like a glove and i actually love the pattern. i ordred the peite to compare, and also the red to see in person... we shall see which is the winnrer!

Categories Output:
 ```json
{
  "Categories": {
    "Division": "General",
    "Department": "Dresses",
    "Class": "Dresses",
    "My Category": "Possible Female Shopper"
  },
  "Review": { 

This is the actual review:
I saw this dress in the new arrivals in red, and it caught my attention, but not enough to order it (the pictures online for other colors didn't look that great). well, i saw the black in the store and decided to try it on as it looked like hte cut would be cute. it fit like a glove and i actually love the pattern. i ordred the peite to compare, and also the red to see in person... we shall see which is the winnrer!

Sentiment Output:
 ```json
{
  "sentiment": "positive"
}
``` 

This is the actual review:
I saw this dress in the new arrivals in red, and it caught my attention, but not enough to order it (the pictures online for other colors didn't look that great). well, i saw the black in the store and decided to try it on as it looked like hte cut would be cute. it fit like a glove and i actually love the pattern. i ordred the peite to compare, and also the red to see in person... we shall see which is the winnrer!

Summary Output:
 ```json
{
  "summary": "The reviewer was initially drawn to the dress in red but was hesitant to order due to online images. After trying on the black version in-store, they found it to fit perfectly and loved the pattern. They 

This is the actual review:
I saw this dress in the new arrivals in red, and it caught my attention, but not enough to order it (the pictures online for other colors didn't look that great). well, i saw the black in the store and decided to try it on as it looked like hte cut would be cute. it fit like a glove and i actually love the pattern. i ordred the peite to compare, and also the red to see in person... we shall see which is the winnrer!

Personalized Message Output:
 ```json
{
  "personalized_message": "Thank you for sharing your experience! We're delighted to hear that the black dress fit you perfectly and that you love the pattern. We hope the petite and red options meet your expectations as well. Happy 

This is the actual review:
I saw this dress in the new arrivals in red, and it caught my attention, but not enough to order it (the pictures online for other colors didn't look that great). well, i saw the black in the store and decided to try it on as it looked like hte cut would be cute. it fit like a glove and i actually love the pattern. i ordred the peite to compare, and also the red to see in person... we shall see which is the winnrer!

Retail Insight Output:
 ```json
{
  "retail_insight": {
    "suggestions": [
      {
        "highlight_features": "Consider highlighting the softness and stylish design in marketing materials, as these features resonate well with customers."
      },
      { 


----- Processing Review 24 (Original Index: 20269) -----
This is the actual review:
This is a beautiful top which runs tts and armholes are not too big for once. i typically struggle with armholes being way too big on me and have to size down in most tops and this top runs tts and armholes are perfect. no bra showing beneath. yay! it is a beautiful color and design. just love it. i am a typical size 4/6 and size 6 was perfect. i am 36c and no problems in bust area. works well with my short torso. highly recommend. i think it will look great on all body types.

Categories Output:
 ```json
{
  "Categories": {
    "Division": "General Petite",
    "Department": "Tops",
    "Class": "Blouses",
    "My Category": "Possible Female Shopper"
  },
  "Review": { 

This is the actual review:
This is a beautiful top which runs tts and armholes are not too big for once. i typically struggle with armholes being way too big on me and have to size down in most tops and this top runs tts and armholes are perfect. no bra showing beneath. yay! it is a beautiful color and design. just love it. i am a typical size 4/6 and size 6 was perfect. i am 36c and no problems in bust area. works well with my short torso. highly recommend. i think it will look great on all body types.

Sentiment Output:
 ```json
{
  "sentiment": "positive"
}
``` 

This is the actual review:
This is a beautiful top which runs tts and armholes are not too big for once. i typically struggle with armholes being way too big on me and have to size down in most tops and this top runs tts and armholes are perfect. no bra showing beneath. yay! it is a beautiful color and design. just love it. i am a typical size 4/6 and size 6 was perfect. i am 36c and no problems in bust area. works well with my short torso. highly recommend. i think it will look great on all body types.

Summary Output:
 ```json
{
  "summary": "The reviewer finds the top to be beautiful and well-fitting, particularly appreciating the perfect armhole size and overall design. They recommend it for all body types."
}
``` 

This is the actual review:
This is a beautiful top which runs tts and armholes are not too big for once. i typically struggle with armholes being way too big on me and have to size down in most tops and this top runs tts and armholes are perfect. no bra showing beneath. yay! it is a beautiful color and design. just love it. i am a typical size 4/6 and size 6 was perfect. i am 36c and no problems in bust area. works well with my short torso. highly recommend. i think it will look great on all body types.

Personalized Message Output:
 ```json
{
  "personalized_message": "Thank you for your wonderful feedback! We're thrilled to hear that you love the fit and design of your new blouse. It's great to know that the armholes and sizing worked perfectly for you. Enjoy 

This is the actual review:
This is a beautiful top which runs tts and armholes are not too big for once. i typically struggle with armholes being way too big on me and have to size down in most tops and this top runs tts and armholes are perfect. no bra showing beneath. yay! it is a beautiful color and design. just love it. i am a typical size 4/6 and size 6 was perfect. i am 36c and no problems in bust area. works well with my short torso. highly recommend. i think it will look great on all body types.

Retail Insight Output:
 ```json
{
  "retail_insight": {
    "suggestions": [
      {
        "feature": "Softness",
        "action": "Highlight the softness of the fabric in marketing materials, as it is a key factor for 


----- Processing Review 25 (Original Index: 20444) -----
This is the actual review:
Usually can just buy tiny shirts knowing that they will fit me!  and this one is beautiful but if you have any type of chest it will not work.  i'm a 34d and it
was way too small in the chest area.  it is a beautiful shirt though and i'm very sad that i have to return it.  it will work better on someone else for sure.  so i recommend this beautiful shirt for someone with a different body type from mine!

Categories Output:
 ```json
{
  "Categories": {
    "Division": "General Petite",
    "Department": "Tops",
    "Class": "Knits",
    "My Category": "Possible Female Shopper"
  },
  "Review": { 

This is the actual review:
Usually can just buy tiny shirts knowing that they will fit me!  and this one is beautiful but if you have any type of chest it will not work.  i'm a 34d and it
was way too small in the chest area.  it is a beautiful shirt though and i'm very sad that i have to return it.  it will work better on someone else for sure.  so i recommend this beautiful shirt for someone with a different body type from mine!

Sentiment Output:
 ```json
{
  "sentiment": "positive"
}
``` 

This is the actual review:
Usually can just buy tiny shirts knowing that they will fit me!  and this one is beautiful but if you have any type of chest it will not work.  i'm a 34d and it
was way too small in the chest area.  it is a beautiful shirt though and i'm very sad that i have to return it.  it will work better on someone else for sure.  so i recommend this beautiful shirt for someone with a different body type from mine!

Summary Output:
 ```json
{
  "summary": "The reviewer finds the pullover beautiful but notes that it does not fit well for those with a larger chest size, specifically mentioning their disappointment in having to return it due to the fit."
}
``` 

This is the actual review:
Usually can just buy tiny shirts knowing that they will fit me!  and this one is beautiful but if you have any type of chest it will not work.  i'm a 34d and it
was way too small in the chest area.  it is a beautiful shirt though and i'm very sad that i have to return it.  it will work better on someone else for sure.  so i recommend this beautiful shirt for someone with a different body type from mine!

Personalized Message Output:
 ```json
{
  "personalized_message": "Thank you for your thoughtful review! We're sorry to hear that the fit didn't work for your body type, but we appreciate your recommendation for others. We hope you find a perfect fit in the future 

This is the actual review:
Usually can just buy tiny shirts knowing that they will fit me!  and this one is beautiful but if you have any type of chest it will not work.  i'm a 34d and it
was way too small in the chest area.  it is a beautiful shirt though and i'm very sad that i have to return it.  it will work better on someone else for sure.  so i recommend this beautiful shirt for someone with a different body type from mine!

Retail Insight Output:
 ```json
{
  "retail_insight": {
    "suggestions": [
      {
        "highlight_features": "Consider emphasizing the softness and stylish design of the tops in marketing materials, as these attributes resonate well with customers."
      }, 


----- Processing Review 26 (Original Index: 20108) -----
This is the actual review:
It drapes perfect and washes well. i own all the colors, they do not disappoint. i'm a medium.

Categories Output:
 ```json
{
  "Categories": {
    "Division": "Intimates",
    "Department": "Intimate",
    "Class": "Layering",
    "My Category": "Possible Female Shopper"
  },
  "Review": { 

This is the actual review:
It drapes perfect and washes well. i own all the colors, they do not disappoint. i'm a medium.

Sentiment Output:
 ```json
{
  "sentiment": "positive"
}
``` 

This is the actual review:
It drapes perfect and washes well. i own all the colors, they do not disappoint. i'm a medium.

Summary Output:
 ```json
{
  "summary": "The reviewer is highly satisfied with the pullover's perfect drape and washability, expressing ownership of all colors without disappointment, and confirming a medium size fit."
}
``` 

This is the actual review:
It drapes perfect and washes well. i own all the colors, they do not disappoint. i'm a medium.

Personalized Message Output:
 ```json
{
  "personalized_message": "Thank you for your wonderful feedback! We're thrilled to hear that you love the fit and quality of your layering pieces. It's great to know that you enjoy all the colors. Happy wearing!"
}
``` 

This is the actual review:
It drapes perfect and washes well. i own all the colors, they do not disappoint. i'm a medium.

Retail Insight Output:
 ```json
{
  "retail_insight": {
    "suggestions": [
      {
        "feature": "softness",
        "action": "Highlight the softness of the fabric in marketing materials, as it is a key factor for 


----- Processing Review 27 (Original Index: 15348) -----
This is the actual review:
It's such a pretty top but i felt that it looked maternity-ish so i returned it.

Categories Output:
 ```json
{
  "Categories": {
    "Division": "General",
    "Department": "Tops",
    "Class": "Knits",
    "My Category": "Possible Female Shopper"
  },
  "Review": { 

This is the actual review:
It's such a pretty top but i felt that it looked maternity-ish so i returned it.

Sentiment Output:
 ```json
{
  "sentiment": "neutral"
}
``` 

This is the actual review:
It's such a pretty top but i felt that it looked maternity-ish so i returned it.

Summary Output:
 ```json
{
  "summary": "The reviewer finds the top visually appealing but feels it has a maternity-like appearance, leading to a return."
}
``` 

This is the actual review:
It's such a pretty top but i felt that it looked maternity-ish so i returned it.

Personalized Message Output:
 ```json
{
  "personalized_message": "Thank you for your feedback! We're sorry to hear that the top didn't meet your expectations in terms of fit. We appreciate your input and hope you find something that you love!"
}
``` 

This is the actual review:
It's such a pretty top but i felt that it looked maternity-ish so i returned it.

Retail Insight Output:
 ```json
{
  "retail_insight": {
    "suggestions": [
      {
        "highlight_features": "Consider emphasizing the softness and stylish design in marketing materials, as these features resonate well with customers."
      },
      { 


----- Processing Review 28 (Original Index: 21209) -----
This is the actual review:
I originally bought this from my local retailer in an xs petite. i think it was a return. i brought it home and decided it was just too big for my 5'4" 116# frame. i ordered it in an xxs petite and am keeping it. the colters are just so pretty and it is machine washable which is an added bonus. it is still flowy but not objectionably so. it is meant to be unstructured and bohemain i think. looking forward to wearing this on my upcoming trip to italy.

Categories Output:
 ```json
{
  "Categories": {
    "Division": "General",
    "Department": "Dresses",
    "Class": "Dresses",
    "My Category": "Possible Female Shopper"
  },
  "Review": { 

This is the actual review:
I originally bought this from my local retailer in an xs petite. i think it was a return. i brought it home and decided it was just too big for my 5'4" 116# frame. i ordered it in an xxs petite and am keeping it. the colters are just so pretty and it is machine washable which is an added bonus. it is still flowy but not objectionably so. it is meant to be unstructured and bohemain i think. looking forward to wearing this on my upcoming trip to italy.

Sentiment Output:
 ```json
{
  "sentiment": "positive"
}
``` 

This is the actual review:
I originally bought this from my local retailer in an xs petite. i think it was a return. i brought it home and decided it was just too big for my 5'4" 116# frame. i ordered it in an xxs petite and am keeping it. the colters are just so pretty and it is machine washable which is an added bonus. it is still flowy but not objectionably so. it is meant to be unstructured and bohemain i think. looking forward to wearing this on my upcoming trip to italy.

Summary Output:
 ```json
{
  "summary": "The reviewer initially found the dress too large in size XS petite but successfully exchanged it for XXS petite, appreciating the beautiful colors and machine washability. They note the dress's flowy, unstructured bo 

This is the actual review:
I originally bought this from my local retailer in an xs petite. i think it was a return. i brought it home and decided it was just too big for my 5'4" 116# frame. i ordered it in an xxs petite and am keeping it. the colters are just so pretty and it is machine washable which is an added bonus. it is still flowy but not objectionably so. it is meant to be unstructured and bohemain i think. looking forward to wearing this on my upcoming trip to italy.

Personalized Message Output:
 ```json
{
  "personalized_message": "Thank you for sharing your experience! We're delighted to hear that the xxs petite fits you perfectly and that you appreciate the beautiful colors and machine washability. Enjoy your upcoming trip to Italy in your 

This is the actual review:
I originally bought this from my local retailer in an xs petite. i think it was a return. i brought it home and decided it was just too big for my 5'4" 116# frame. i ordered it in an xxs petite and am keeping it. the colters are just so pretty and it is machine washable which is an added bonus. it is still flowy but not objectionably so. it is meant to be unstructured and bohemain i think. looking forward to wearing this on my upcoming trip to italy.

Retail Insight Output:
 ```json
{
  "retail_insight": {
    "suggestions": [
      {
        "feature": "Softness",
        "action": "Highlight the softness of the fabric in marketing materials, as it is a key factor for 


----- Processing Review 29 (Original Index: 17627) -----
This is the actual review:
I'm 10mths post partum so my belly is still flabby. the elastic is too tight on my waist. i wish this was looser with a drawstring to tighten. i have to return it.

Categories Output:
 ```json
{
  "Categories": {
    "Division": "General",
    "Department": "Bottoms",
    "Class": "Pants",
    "My Category": "Possible Female Shopper"
  },
  "Review": { 

This is the actual review:
I'm 10mths post partum so my belly is still flabby. the elastic is too tight on my waist. i wish this was looser with a drawstring to tighten. i have to return it.

Sentiment Output:
 ```json
{
  "sentiment": "negative"
}
``` 

This is the actual review:
I'm 10mths post partum so my belly is still flabby. the elastic is too tight on my waist. i wish this was looser with a drawstring to tighten. i have to return it.

Summary Output:
 ```json
{
  "summary": "The reviewer, 10 months postpartum, finds the elastic waist of the pants too tight and wishes for a looser fit with a drawstring for adjustment, leading to a decision to return the item."
} 

This is the actual review:
I'm 10mths post partum so my belly is still flabby. the elastic is too tight on my waist. i wish this was looser with a drawstring to tighten. i have to return it.

Personalized Message Output:
 ```json
{
  "personalized_message": "Thank you for your feedback! We're sorry to hear that the fit of the pants didn't meet your expectations. We appreciate your suggestion for a looser design with a drawstring, and we will take 

This is the actual review:
I'm 10mths post partum so my belly is still flabby. the elastic is too tight on my waist. i wish this was looser with a drawstring to tighten. i have to return it.

Retail Insight Output:
 ```json
{
  "retail_insight": {
    "suggestions": [
      {
        "feature": "Elastic Waistband",
        "insight": "Consider offering a looser elastic waistband option or incorporating a drawstring feature to 


----- Processing Review 30 (Original Index: 872) -----
This is the actual review:
These pants are perfect!!! they are exactly what i was looking for, and worth every penny! the fabric is more lightweight than any other sort of denim, but still i think i could wear these any season. they look so cute with simple heels and a classic blouse, but they can be dressed down with nice sneakers and a plain white t! so excited to have these in my closet!! love a good pair of fun pants.

Categories Output:
 ```json
{
  "Categories": {
    "Division": "General",
    "Department": "Bottoms",
    "Class": "Pants",
    "My Category": "Possible Female Shopper"
  },
  "Review": { 

This is the actual review:
These pants are perfect!!! they are exactly what i was looking for, and worth every penny! the fabric is more lightweight than any other sort of denim, but still i think i could wear these any season. they look so cute with simple heels and a classic blouse, but they can be dressed down with nice sneakers and a plain white t! so excited to have these in my closet!! love a good pair of fun pants.

Sentiment Output:
 ```json
{
  "sentiment": "positive"
}
``` 

This is the actual review:
These pants are perfect!!! they are exactly what i was looking for, and worth every penny! the fabric is more lightweight than any other sort of denim, but still i think i could wear these any season. they look so cute with simple heels and a classic blouse, but they can be dressed down with nice sneakers and a plain white t! so excited to have these in my closet!! love a good pair of fun pants.

Summary Output:
 ```json
{
  "summary": "The reviewer is extremely satisfied with the pants, praising their lightweight fabric, versatility for all seasons, and stylish appearance when paired with different outfits."
}
``` 

This is the actual review:
These pants are perfect!!! they are exactly what i was looking for, and worth every penny! the fabric is more lightweight than any other sort of denim, but still i think i could wear these any season. they look so cute with simple heels and a classic blouse, but they can be dressed down with nice sneakers and a plain white t! so excited to have these in my closet!! love a good pair of fun pants.

Personalized Message Output:
 ```json
{
  "personalized_message": "Thank you for your wonderful feedback! We're thrilled to hear that you love the versatility and lightweight fabric of your new pants. Enjoy styling them for any occasion!"
}
``` 

This is the actual review:
These pants are perfect!!! they are exactly what i was looking for, and worth every penny! the fabric is more lightweight than any other sort of denim, but still i think i could wear these any season. they look so cute with simple heels and a classic blouse, but they can be dressed down with nice sneakers and a plain white t! so excited to have these in my closet!! love a good pair of fun pants.

Retail Insight Output:
 ```json
{
  "retail_insight": {
    "suggestions": [
      "Highlight the softness and stylish design in marketing materials, as these features resonate well with customers.",
      "Showcase customer fit examples to help potential buyers make 


----- Processing Review 31 (Original Index: 7209) -----
This is the actual review:
This was such a nice surprise. it's way more sweatshirt than i thought, and maybe a touch bigger than i thought, but it's super cute and comfortable. i've worn it a bunch of times already.

Categories Output:
 ```json
{
  "Categories": {
    "Division": "General",
    "Department": "Tops",
    "Class": "Knits",
    "My Category": "Possible Female Shopper"
  },
  "Review": { 

This is the actual review:
This was such a nice surprise. it's way more sweatshirt than i thought, and maybe a touch bigger than i thought, but it's super cute and comfortable. i've worn it a bunch of times already.

Sentiment Output:
 ```json
{
  "sentiment": "positive"
}
``` 

This is the actual review:
This was such a nice surprise. it's way more sweatshirt than i thought, and maybe a touch bigger than i thought, but it's super cute and comfortable. i've worn it a bunch of times already.

Summary Output:
 ```json
{
  "summary": "The reviewer finds the pullover to be a pleasant surprise, noting its comfort and cuteness, although it is slightly larger than expected."
}
``` 

This is the actual review:
This was such a nice surprise. it's way more sweatshirt than i thought, and maybe a touch bigger than i thought, but it's super cute and comfortable. i've worn it a bunch of times already.

Personalized Message Output:
 ```json
{
  "personalized_message": "Thank you for your wonderful feedback! We're thrilled to hear that you found the pullover to be a nice surprise and that it's both cute and comfortable. Enjoy wearing it!"
}
``` 

This is the actual review:
This was such a nice surprise. it's way more sweatshirt than i thought, and maybe a touch bigger than i thought, but it's super cute and comfortable. i've worn it a bunch of times already.

Retail Insight Output:
 ```json
{
  "retail_insight": {
    "suggestions": [
      {
        "feature": "softness",
        "action": "Highlight the softness of the fabric in marketing materials to attract customers who prioritize comfort." 


----- Processing Review 32 (Original Index: 20360) -----
This is the actual review:
This shirt looked so perfect on the model, and i was so excited to get it! when i received it, it was so soft and the color was so vibrant. the only thing is, it's too big. it kind of looks like a maternity shirt. it will definitely have to be taken in on the sides for me, but i'm happy with it other than that. i'm normally a l/xl but m would have been perfect this time.

Categories Output:
 ```json
{
  "Categories": {
    "Division": "General",
    "Department": "Tops",
    "Class": "Knits",
    "My Category": "Possible Female Shopper"
  },
  "Review": { 

This is the actual review:
This shirt looked so perfect on the model, and i was so excited to get it! when i received it, it was so soft and the color was so vibrant. the only thing is, it's too big. it kind of looks like a maternity shirt. it will definitely have to be taken in on the sides for me, but i'm happy with it other than that. i'm normally a l/xl but m would have been perfect this time.

Sentiment Output:
 ```json
{
  "sentiment": "positive"
}
``` 

This is the actual review:
This shirt looked so perfect on the model, and i was so excited to get it! when i received it, it was so soft and the color was so vibrant. the only thing is, it's too big. it kind of looks like a maternity shirt. it will definitely have to be taken in on the sides for me, but i'm happy with it other than that. i'm normally a l/xl but m would have been perfect this time.

Summary Output:
 ```json
{
  "summary": "The reviewer loves the softness and vibrant color of the shirt but finds it too big, resembling a maternity shirt, and suggests a smaller size would have been better."
}
``` 

This is the actual review:
This shirt looked so perfect on the model, and i was so excited to get it! when i received it, it was so soft and the color was so vibrant. the only thing is, it's too big. it kind of looks like a maternity shirt. it will definitely have to be taken in on the sides for me, but i'm happy with it other than that. i'm normally a l/xl but m would have been perfect this time.

Personalized Message Output:
 ```json
{
  "personalized_message": "Thank you for your wonderful feedback! We're thrilled to hear that you love the softness and vibrant color of your new shirt. We appreciate your input regarding the sizing and will take it into consideration for future 

This is the actual review:
This shirt looked so perfect on the model, and i was so excited to get it! when i received it, it was so soft and the color was so vibrant. the only thing is, it's too big. it kind of looks like a maternity shirt. it will definitely have to be taken in on the sides for me, but i'm happy with it other than that. i'm normally a l/xl but m would have been perfect this time.

Retail Insight Output:
 ```json
{
  "retail_insight": {
    "suggestions": [
      {
        "highlight_features": "Consider highlighting the softness and stylish design in marketing materials, as these features resonate well with customers."
      },
      { 


----- Processing Review 33 (Original Index: 20819) -----
This is the actual review:
I love this dress in the photos, and like it enough to keep it--but i don't love it. it runs borderline true-to-size/slightly large. i'm 5'8", 133 lbs, 34 b and usually wear a 6 in dresses. i ordered a sm and med. am keeping the sm. medium was way too big. my main issue is the amount of fabric in the skirt--it's a bit full. in the pics, this dress appears straight, but it's not. the higher waist and skirt's fullness gives it a more casual, youthful look, which is not what i want. but the quality

Categories Output:
 ```json
{
  "Categories": {
    "Division": "General Petite",
    "Department": "Dresses",
    "Class": "Dresses",
    "My Category": "Possible Female Shopper"
  },
  "Review": { 

This is the actual review:
I love this dress in the photos, and like it enough to keep it--but i don't love it. it runs borderline true-to-size/slightly large. i'm 5'8", 133 lbs, 34 b and usually wear a 6 in dresses. i ordered a sm and med. am keeping the sm. medium was way too big. my main issue is the amount of fabric in the skirt--it's a bit full. in the pics, this dress appears straight, but it's not. the higher waist and skirt's fullness gives it a more casual, youthful look, which is not what i want. but the quality

Sentiment Output:
 ```json
{
  "sentiment": "neutral",
  "review": {
    "division": "General Petite",
    "department": "Dresses",
    "class": "Dresses",
    "content": "I love this dress 

This is the actual review:
I love this dress in the photos, and like it enough to keep it--but i don't love it. it runs borderline true-to-size/slightly large. i'm 5'8", 133 lbs, 34 b and usually wear a 6 in dresses. i ordered a sm and med. am keeping the sm. medium was way too big. my main issue is the amount of fabric in the skirt--it's a bit full. in the pics, this dress appears straight, but it's not. the higher waist and skirt's fullness gives it a more casual, youthful look, which is not what i want. but the quality

Summary Output:
 ```json
{
  "summary": "The reviewer finds the dress visually appealing but is not fully satisfied with its fit and style. They note that it runs slightly large and has an unexpectedly full skirt, which gives it a casual look that does not 

This is the actual review:
I love this dress in the photos, and like it enough to keep it--but i don't love it. it runs borderline true-to-size/slightly large. i'm 5'8", 133 lbs, 34 b and usually wear a 6 in dresses. i ordered a sm and med. am keeping the sm. medium was way too big. my main issue is the amount of fabric in the skirt--it's a bit full. in the pics, this dress appears straight, but it's not. the higher waist and skirt's fullness gives it a more casual, youthful look, which is not what i want. but the quality

Personalized Message Output:
 ```json
{
  "personalized_message": "Thank you for sharing your thoughts on the dress! We appreciate your feedback regarding the fit and style. It's great to hear that you found the small size to be a better fit for you. We 

This is the actual review:
I love this dress in the photos, and like it enough to keep it--but i don't love it. it runs borderline true-to-size/slightly large. i'm 5'8", 133 lbs, 34 b and usually wear a 6 in dresses. i ordered a sm and med. am keeping the sm. medium was way too big. my main issue is the amount of fabric in the skirt--it's a bit full. in the pics, this dress appears straight, but it's not. the higher waist and skirt's fullness gives it a more casual, youthful look, which is not what i want. but the quality

Retail Insight Output:
 ```json
{
  "retail_insight": {
    "suggestions": [
      {
        "highlight_features": "Emphasize the softness and stylish design of the dress in marketing materials, as these attributes resonate well with customers." 


----- Processing Review 34 (Original Index: 965) -----
This is the actual review:
I love sweater knit dresses and this the first one i've owned with a flare style that works. pattern placement is gorgeously done and is very slimming in a non-obvious way. i think the fabric is soft and warm. i wish the scoop neckline was a tad bit higher from a modesty standpoint. you may want to size down if you're not busty. i am 5'3" and m petite was the perfect length on me. shout out to the reno distribution center for getting this amazing dress to me in 1.5 days?!?!?!!!

Categories Output:
 ```json
{
  "Categories": {
    "Division": "General",
    "Department": "Dresses",
    "Class": "Dresses",
    "My Category": "Possible Female Shopper"
  },
  "Review": { 

This is the actual review:
I love sweater knit dresses and this the first one i've owned with a flare style that works. pattern placement is gorgeously done and is very slimming in a non-obvious way. i think the fabric is soft and warm. i wish the scoop neckline was a tad bit higher from a modesty standpoint. you may want to size down if you're not busty. i am 5'3" and m petite was the perfect length on me. shout out to the reno distribution center for getting this amazing dress to me in 1.5 days?!?!?!!!

Sentiment Output:
 ```json
{
  "sentiment": "positive"
}
``` 

This is the actual review:
I love sweater knit dresses and this the first one i've owned with a flare style that works. pattern placement is gorgeously done and is very slimming in a non-obvious way. i think the fabric is soft and warm. i wish the scoop neckline was a tad bit higher from a modesty standpoint. you may want to size down if you're not busty. i am 5'3" and m petite was the perfect length on me. shout out to the reno distribution center for getting this amazing dress to me in 1.5 days?!?!?!!!

Summary Output:
 ```json
{
  "summary": "The reviewer loves the sweater knit dress for its flattering flare style and beautiful pattern placement, noting its softness and warmth. They suggest sizing down for those with smaller busts and mention the perfect length for petite sizes 

This is the actual review:
I love sweater knit dresses and this the first one i've owned with a flare style that works. pattern placement is gorgeously done and is very slimming in a non-obvious way. i think the fabric is soft and warm. i wish the scoop neckline was a tad bit higher from a modesty standpoint. you may want to size down if you're not busty. i am 5'3" and m petite was the perfect length on me. shout out to the reno distribution center for getting this amazing dress to me in 1.5 days?!?!?!!!

Personalized Message Output:
 ```json
{
  "personalized_message": "Thank you for your fantastic review! We're delighted to hear that you love the fit and fabric of your new sweater knit dress. Your feedback on the neckline and sizing is valuable, and we appreciate your 

This is the actual review:
I love sweater knit dresses and this the first one i've owned with a flare style that works. pattern placement is gorgeously done and is very slimming in a non-obvious way. i think the fabric is soft and warm. i wish the scoop neckline was a tad bit higher from a modesty standpoint. you may want to size down if you're not busty. i am 5'3" and m petite was the perfect length on me. shout out to the reno distribution center for getting this amazing dress to me in 1.5 days?!?!?!!!

Retail Insight Output:
 ```json
{
  "retail_insight": {
    "suggestions": [
      {
        "feature": "Softness",
        "action": "Highlight the softness of the fabric in marketing materials, as it is a key factor for 


----- Processing Review 35 (Original Index: 5127) -----
This is the actual review:
I love this poncho!! it's perfect for chilling saturday mornings at the soccer field. it's a nice warm layer, but doesn't get to hot. pair it with a long sleeve shirt, jeans and some boots and you're set.

Categories Output:
 ```json
{
  "Categories": {
    "Division": "General",
    "Department": "Tops",
    "Class": "Fine gauge",
    "My Category": "Possible Female Shopper"
  },
  "Review": { 

This is the actual review:
I love this poncho!! it's perfect for chilling saturday mornings at the soccer field. it's a nice warm layer, but doesn't get to hot. pair it with a long sleeve shirt, jeans and some boots and you're set.

Sentiment Output:
 ```json
{
  "sentiment": "positive"
}
``` 

This is the actual review:
I love this poncho!! it's perfect for chilling saturday mornings at the soccer field. it's a nice warm layer, but doesn't get to hot. pair it with a long sleeve shirt, jeans and some boots and you're set.

Summary Output:
 ```json
{
  "summary": "The reviewer loves the poncho for its warmth and comfort, finding it perfect for casual outings, especially during chilly mornings at the soccer field."
}
``` 

This is the actual review:
I love this poncho!! it's perfect for chilling saturday mornings at the soccer field. it's a nice warm layer, but doesn't get to hot. pair it with a long sleeve shirt, jeans and some boots and you're set.

Personalized Message Output:
 ```json
{
  "personalized_message": "Thank you for your wonderful feedback! We're thrilled to hear that you love your new poncho and that it's perfect for your Saturday mornings at the soccer field. Enjoy your stylish and comfortable look!"
} 

This is the actual review:
I love this poncho!! it's perfect for chilling saturday mornings at the soccer field. it's a nice warm layer, but doesn't get to hot. pair it with a long sleeve shirt, jeans and some boots and you're set.

Retail Insight Output:
 ```json
{
  "retail_insight": {
    "suggestions": [
      "Highlight the softness and stylish design in marketing materials, as these features resonate well with customers.",
      "Showcase customer fit examples to help potential buyers make 


----- Processing Review 36 (Original Index: 19923) -----
This is the actual review:
I love this tank. i got it in the turquoise and just ordered the blue. this tank is indeed linen and does not wrinkle. it's fitted without being too tight. i have a somewhat shorter torso and narrow waist and athletic build and this looks great! i'm not a fan of flowy tops in general so thrilled to find this easy, casual piece. i ordered an xs but am usually either xs or s in retailer.

Categories Output:
 ```json
{
  "Categories": {
    "Division": "General Petite",
    "Department": "Tops",
    "Class": "Knits",
    "My Category": "Possible Female Shopper"
  },
  "Review": { 

This is the actual review:
I love this tank. i got it in the turquoise and just ordered the blue. this tank is indeed linen and does not wrinkle. it's fitted without being too tight. i have a somewhat shorter torso and narrow waist and athletic build and this looks great! i'm not a fan of flowy tops in general so thrilled to find this easy, casual piece. i ordered an xs but am usually either xs or s in retailer.

Sentiment Output:
 ```json
{
  "sentiment": "positive"
}
``` 

This is the actual review:
I love this tank. i got it in the turquoise and just ordered the blue. this tank is indeed linen and does not wrinkle. it's fitted without being too tight. i have a somewhat shorter torso and narrow waist and athletic build and this looks great! i'm not a fan of flowy tops in general so thrilled to find this easy, casual piece. i ordered an xs but am usually either xs or s in retailer.

Summary Output:
 ```json
{
  "summary": "The reviewer loves the fitted design and wrinkle-free quality of the linen tank, appreciating its flattering fit for a shorter torso and athletic build. They are pleased with the casual style and have ordered additional colors."
} 

This is the actual review:
I love this tank. i got it in the turquoise and just ordered the blue. this tank is indeed linen and does not wrinkle. it's fitted without being too tight. i have a somewhat shorter torso and narrow waist and athletic build and this looks great! i'm not a fan of flowy tops in general so thrilled to find this easy, casual piece. i ordered an xs but am usually either xs or s in retailer.

Personalized Message Output:
 ```json
{
  "personalized_message": "Thank you for your wonderful feedback! We're thrilled to hear that you love the softness and style of your new tank. It's great to know that the fit works well for your body type. Enjoy wearing 

This is the actual review:
I love this tank. i got it in the turquoise and just ordered the blue. this tank is indeed linen and does not wrinkle. it's fitted without being too tight. i have a somewhat shorter torso and narrow waist and athletic build and this looks great! i'm not a fan of flowy tops in general so thrilled to find this easy, casual piece. i ordered an xs but am usually either xs or s in retailer.

Retail Insight Output:
 ```json
{
  "retail_insight": {
    "suggestions": [
      {
        "feature": "softness",
        "action": "Highlight the softness of the fabric in marketing materials, as it is a key factor for 


----- Processing Review 37 (Original Index: 22116) -----
This is the actual review:
I purchased this dress for the second time: it is the gingham garden dress from 2015 that was blue and white. same dress, new name and color. generally, this dress is large overall and long. i wish i could have purchased the petite size. i liked it enough to have it in black and white. this time around i ordered one size down and fit is much better.
recommendation: order 1 size down from your normal sizing as dress runs large.
the quality of the knit material is soft and flowing.

Categories Output:
 ```json
{
  "Categories": {
    "Division": "General",
    "Department": "Dresses",
    "Class": "Dresses",
    "My Category": "Possible Female Shopper"
  },
  "Review": { 

This is the actual review:
I purchased this dress for the second time: it is the gingham garden dress from 2015 that was blue and white. same dress, new name and color. generally, this dress is large overall and long. i wish i could have purchased the petite size. i liked it enough to have it in black and white. this time around i ordered one size down and fit is much better.
recommendation: order 1 size down from your normal sizing as dress runs large.
the quality of the knit material is soft and flowing.

Sentiment Output:
 ```json
{
  "sentiment": "positive",
  "review": {
    "division": "General",
    "department": "Dresses",
    "class": "Dresses",
    "content": "I purchased this dress for 

This is the actual review:
I purchased this dress for the second time: it is the gingham garden dress from 2015 that was blue and white. same dress, new name and color. generally, this dress is large overall and long. i wish i could have purchased the petite size. i liked it enough to have it in black and white. this time around i ordered one size down and fit is much better.
recommendation: order 1 size down from your normal sizing as dress runs large.
the quality of the knit material is soft and flowing.

Summary Output:
 ```json
{
  "summary": "The reviewer expresses satisfaction with the gingham garden dress, noting its soft and flowing knit material. They recommend ordering one size down due to the dress running large, and mention that the fit improved with the smaller 

This is the actual review:
I purchased this dress for the second time: it is the gingham garden dress from 2015 that was blue and white. same dress, new name and color. generally, this dress is large overall and long. i wish i could have purchased the petite size. i liked it enough to have it in black and white. this time around i ordered one size down and fit is much better.
recommendation: order 1 size down from your normal sizing as dress runs large.
the quality of the knit material is soft and flowing.

Personalized Message Output:
 ```json
{
  "personalized_message": "Thank you for your detailed feedback! We're glad to hear that you love the gingham garden dress enough to purchase it again. It's great to know that sizing down worked for you and that you appreciate 

This is the actual review:
I purchased this dress for the second time: it is the gingham garden dress from 2015 that was blue and white. same dress, new name and color. generally, this dress is large overall and long. i wish i could have purchased the petite size. i liked it enough to have it in black and white. this time around i ordered one size down and fit is much better.
recommendation: order 1 size down from your normal sizing as dress runs large.
the quality of the knit material is soft and flowing.

Retail Insight Output:
 ```json
{
  "retail_insight": {
    "suggestions": [
      {
        "highlight_features": "Emphasize the softness and stylish design of the dress in marketing materials, as these attributes resonate well with customers." 


----- Processing Review 38 (Original Index: 10851) -----
This is the actual review:
The tank top fits well. i usually boarded between a large and an xl and i got a large in this shirt which was perfect. it is kind of thin material so i wear a white tank under it. it's not too big in the armpits which is nice so my bra doesn't show. material is really soft and comfortable. it fits loose but not baggy.

Categories Output:
 ```json
{
  "Categories": {
    "Division": "General",
    "Department": "Tops",
    "Class": "Knits",
    "My Category": "Possible Female Shopper"
  },
  "Review": { 

This is the actual review:
The tank top fits well. i usually boarded between a large and an xl and i got a large in this shirt which was perfect. it is kind of thin material so i wear a white tank under it. it's not too big in the armpits which is nice so my bra doesn't show. material is really soft and comfortable. it fits loose but not baggy.

Sentiment Output:
 ```json
{
  "sentiment": "positive"
}
``` 

This is the actual review:
The tank top fits well. i usually boarded between a large and an xl and i got a large in this shirt which was perfect. it is kind of thin material so i wear a white tank under it. it's not too big in the armpits which is nice so my bra doesn't show. material is really soft and comfortable. it fits loose but not baggy.

Summary Output:
 ```json
{
  "summary": "The reviewer finds the tank top to fit perfectly, appreciating its soft material and comfortable loose fit, while also noting the benefit of not being too big in the armpits."
}
``` 

This is the actual review:
The tank top fits well. i usually boarded between a large and an xl and i got a large in this shirt which was perfect. it is kind of thin material so i wear a white tank under it. it's not too big in the armpits which is nice so my bra doesn't show. material is really soft and comfortable. it fits loose but not baggy.

Personalized Message Output:
 ```json
{
  "personalized_message": "Thank you for your detailed review! We're delighted to hear that the fit of the tank top is just right for you and that you appreciate the softness and comfort. It's great to know that the design 

This is the actual review:
The tank top fits well. i usually boarded between a large and an xl and i got a large in this shirt which was perfect. it is kind of thin material so i wear a white tank under it. it's not too big in the armpits which is nice so my bra doesn't show. material is really soft and comfortable. it fits loose but not baggy.

Retail Insight Output:
 ```json
{
  "retail_insight": {
    "suggestions": [
      {
        "feature": "Softness",
        "action": "Highlight the softness of the material in marketing materials, as it is a key factor for 


----- Processing Review 39 (Original Index: 10213) -----
This is the actual review:
This top is really comfortable but dressy enough for a business/casual office. the fit is nice and loose without making you look larger than you are. i get a lot of compliments every time i wear this.

Categories Output:
 ```json
{
  "Categories": {
    "Division": "General Petite",
    "Department": "Tops",
    "Class": "Fine gauge",
    "My Category": "Possible Female Shopper"
  },
  "Review": { 

This is the actual review:
This top is really comfortable but dressy enough for a business/casual office. the fit is nice and loose without making you look larger than you are. i get a lot of compliments every time i wear this.

Sentiment Output:
 ```json
{
  "sentiment": "positive"
}
``` 

This is the actual review:
This top is really comfortable but dressy enough for a business/casual office. the fit is nice and loose without making you look larger than you are. i get a lot of compliments every time i wear this.

Summary Output:
 ```json
{
  "summary": "The reviewer finds the top comfortable and suitable for a business/casual office setting, highlighting its nice loose fit that flatters the figure and noting that they receive many compliments when wearing it."
}
``` 

This is the actual review:
This top is really comfortable but dressy enough for a business/casual office. the fit is nice and loose without making you look larger than you are. i get a lot of compliments every time i wear this.

Personalized Message Output:
 ```json
{
  "personalized_message": "Thank you for your wonderful feedback! We're thrilled to hear that you find the top comfortable and suitable for a business/casual office. It's great to know that the fit works well for you and 

This is the actual review:
This top is really comfortable but dressy enough for a business/casual office. the fit is nice and loose without making you look larger than you are. i get a lot of compliments every time i wear this.

Retail Insight Output:
 ```json
{
  "retail_insight": {
    "suggestions": [
      {
        "highlight_features": "Consider highlighting the softness and stylish design in marketing materials, as these features resonate well with customers."
      },
      { 


----- Processing Review 40 (Original Index: 3678) -----
This is the actual review:
Definitely pj quality. i have a fairly thin frame and this hung on me like a tent. and it's very thin, so it works as a swim cover, but definitely not a dress for anything else.

Categories Output:
 ```json
{
  "Categories": {
    "Division": "General Petite",
    "Department": "Intimate",
    "Class": "Lounge",
    "My Category": "Possible Female Shopper"
  },
  "Review": { 

This is the actual review:
Definitely pj quality. i have a fairly thin frame and this hung on me like a tent. and it's very thin, so it works as a swim cover, but definitely not a dress for anything else.

Sentiment Output:
 ```json
{
  "sentiment": "negative"
}
``` 

This is the actual review:
Definitely pj quality. i have a fairly thin frame and this hung on me like a tent. and it's very thin, so it works as a swim cover, but definitely not a dress for anything else.

Summary Output:
 ```json
{
  "summary": "The reviewer finds the pullover to be of pajama quality, noting that it fits loosely on their thin frame and is too thin for regular wear, although it could serve as a swim cover."
}
``` 

This is the actual review:
Definitely pj quality. i have a fairly thin frame and this hung on me like a tent. and it's very thin, so it works as a swim cover, but definitely not a dress for anything else.

Personalized Message Output:
 ```json
{
  "personalized_message": "Thank you for your feedback! We're sorry to hear that the fit and quality of the lounge piece did not meet your expectations. We appreciate your input and will take it into consideration for future designs." 

This is the actual review:
Definitely pj quality. i have a fairly thin frame and this hung on me like a tent. and it's very thin, so it works as a swim cover, but definitely not a dress for anything else.

Retail Insight Output:
 ```json
{
  "retail_insight": {
    "suggestions": [
      {
        "highlight_features": "Consider emphasizing the softness and stylish design in marketing materials, as these features resonate well with customers."
      },
      { 


----- Processing Review 41 (Original Index: 12349) -----
This is the actual review:
Love this! when i first saw it online, i thought it was a bit pricey for a plain white shirt. but i finally gave in and ordered it! i'm so glad i did! i've worn it twice in one week so it won't be difficult to get my money's worth! it's very soft and fits perfectly. looks great with jeans or dress pants. i like to tuck the front in my pants and leave the back out.

Categories Output:
 ```json
{
  "Categories": {
    "Division": "General",
    "Department": "Tops",
    "Class": "Blouses",
    "My Category": "Possible Female Shopper"
  },
  "Review": { 

This is the actual review:
Love this! when i first saw it online, i thought it was a bit pricey for a plain white shirt. but i finally gave in and ordered it! i'm so glad i did! i've worn it twice in one week so it won't be difficult to get my money's worth! it's very soft and fits perfectly. looks great with jeans or dress pants. i like to tuck the front in my pants and leave the back out.

Sentiment Output:
 ```json
{
  "sentiment": "positive"
}
``` 

This is the actual review:
Love this! when i first saw it online, i thought it was a bit pricey for a plain white shirt. but i finally gave in and ordered it! i'm so glad i did! i've worn it twice in one week so it won't be difficult to get my money's worth! it's very soft and fits perfectly. looks great with jeans or dress pants. i like to tuck the front in my pants and leave the back out.

Summary Output:
 ```json
{
  "summary": "The reviewer initially hesitated due to the price of the plain white shirt but ultimately found it worth the investment, praising its softness, perfect fit, and versatility with different outfits."
}
``` 

This is the actual review:
Love this! when i first saw it online, i thought it was a bit pricey for a plain white shirt. but i finally gave in and ordered it! i'm so glad i did! i've worn it twice in one week so it won't be difficult to get my money's worth! it's very soft and fits perfectly. looks great with jeans or dress pants. i like to tuck the front in my pants and leave the back out.

Personalized Message Output:
 ```json
{
  "personalized_message": "Thank you for your wonderful feedback! We're thrilled to hear that you love the softness and fit of your new blouse. It's great to know that it's versatile enough to pair with both jeans and dress pants 

This is the actual review:
Love this! when i first saw it online, i thought it was a bit pricey for a plain white shirt. but i finally gave in and ordered it! i'm so glad i did! i've worn it twice in one week so it won't be difficult to get my money's worth! it's very soft and fits perfectly. looks great with jeans or dress pants. i like to tuck the front in my pants and leave the back out.

Retail Insight Output:
 ```json
{
  "retail_insight": {
    "suggestions": [
      {
        "feature": "softness",
        "action": "Highlight the softness of the fabric in marketing materials, as it is a key factor in 


----- Processing Review 42 (Original Index: 10082) -----
This is the actual review:
Bought this shirt on-line. wanted to love it but just couldn't. i ordered the xs and it was huge and way too unstructured for my style....just didn't have enough shape. had to send it back.

Categories Output:
 ```json
{
  "Categories": {
    "Division": "General",
    "Department": "Tops",
    "Class": "Knits",
    "My Category": "Possible Female Shopper"
  },
  "Review": { 

This is the actual review:
Bought this shirt on-line. wanted to love it but just couldn't. i ordered the xs and it was huge and way too unstructured for my style....just didn't have enough shape. had to send it back.

Sentiment Output:
 ```json
{
  "sentiment": "negative"
}
``` 

This is the actual review:
Bought this shirt on-line. wanted to love it but just couldn't. i ordered the xs and it was huge and way too unstructured for my style....just didn't have enough shape. had to send it back.

Summary Output:
 ```json
{
  "summary": "The reviewer was disappointed with the pullover, finding the XS size to be oversized and lacking structure, leading to a return."
}
``` 

This is the actual review:
Bought this shirt on-line. wanted to love it but just couldn't. i ordered the xs and it was huge and way too unstructured for my style....just didn't have enough shape. had to send it back.

Personalized Message Output:
 ```json
{
  "personalized_message": "Thank you for your feedback! We're sorry to hear that the fit and structure of the shirt didn't meet your expectations. We appreciate your input and will take it into consideration as we continue to improve our 

This is the actual review:
Bought this shirt on-line. wanted to love it but just couldn't. i ordered the xs and it was huge and way too unstructured for my style....just didn't have enough shape. had to send it back.

Retail Insight Output:
 ```json
{
  "retail_insight": {
    "suggestions": [
      {
        "feature": "Softness",
        "action": "Highlight the softness of the fabric in marketing materials to attract customers who prioritize comfort." 


----- Processing Review 43 (Original Index: 8201) -----
This is the actual review:
I've never bought a tee at full price, in so many colors. i love this tee.it's quite possibly the most perfect fit ever. the material is thick and hangs well without being stiff or bulky. i'm 5'2 -117lbs usually a small petite or 4p but purchased the regular small so i can wear this with skinny or regular jeans. i purchased the purple, navy and black and may go back for the yellow. the white is still a little too sheer fir me otherwise i would have purchased it. wish they had a light grey versio

Categories Output:
 ```json
{
  "Categories": {
    "Division": "General",
    "Department": "Tops",
    "Class": "Knits",
    "My Category": "Possible Female Shopper"
  },
  "Review": { 

This is the actual review:
I've never bought a tee at full price, in so many colors. i love this tee.it's quite possibly the most perfect fit ever. the material is thick and hangs well without being stiff or bulky. i'm 5'2 -117lbs usually a small petite or 4p but purchased the regular small so i can wear this with skinny or regular jeans. i purchased the purple, navy and black and may go back for the yellow. the white is still a little too sheer fir me otherwise i would have purchased it. wish they had a light grey versio

Sentiment Output:
 ```json
{
  "sentiment": "positive"
}
``` 

This is the actual review:
I've never bought a tee at full price, in so many colors. i love this tee.it's quite possibly the most perfect fit ever. the material is thick and hangs well without being stiff or bulky. i'm 5'2 -117lbs usually a small petite or 4p but purchased the regular small so i can wear this with skinny or regular jeans. i purchased the purple, navy and black and may go back for the yellow. the white is still a little too sheer fir me otherwise i would have purchased it. wish they had a light grey versio

Summary Output:
 ```json
{
  "summary": "The reviewer is highly satisfied with the tee's perfect fit and quality material, expressing a desire to purchase more colors despite finding the white option too sheer."
}
``` 

This is the actual review:
I've never bought a tee at full price, in so many colors. i love this tee.it's quite possibly the most perfect fit ever. the material is thick and hangs well without being stiff or bulky. i'm 5'2 -117lbs usually a small petite or 4p but purchased the regular small so i can wear this with skinny or regular jeans. i purchased the purple, navy and black and may go back for the yellow. the white is still a little too sheer fir me otherwise i would have purchased it. wish they had a light grey versio

Personalized Message Output:
 ```json
{
  "personalized_message": "Thank you for your fantastic review! We're delighted to hear that you found the perfect fit and love the variety of colors. Your feedback about the material and fit is invaluable, and we appreciate your suggestion 

This is the actual review:
I've never bought a tee at full price, in so many colors. i love this tee.it's quite possibly the most perfect fit ever. the material is thick and hangs well without being stiff or bulky. i'm 5'2 -117lbs usually a small petite or 4p but purchased the regular small so i can wear this with skinny or regular jeans. i purchased the purple, navy and black and may go back for the yellow. the white is still a little too sheer fir me otherwise i would have purchased it. wish they had a light grey versio

Retail Insight Output:
 ```json
{
  "retail_insight": {
    "suggestions": [
      {
        "feature": "Softness",
        "action": "Highlight the softness of the material in marketing materials to attract customers who prioritize comfort." 


----- Processing Review 44 (Original Index: 11480) -----
This is the actual review:
I saw this jacket in stores, but did not buy it. i went home, but could not get this trendy jacket out of my head. i returned to the store the very next day and purchased this item. i am very pleased with the quality and think that the fit could go well on most body types. i got the denim color, which may not be the best color for all, but i love it because denim is timeless and very versatile. i would suggest this to anyone and could not be happier with this! go out and buy it now, you won't re

Categories Output:
 ```json
{
  "Categories": {
    "Division": "General Petite",
    "Department": "Jackets",
    "Class": "Jackets",
    "My Category": "Possible Female Shopper"
  },
  "Review": { 

This is the actual review:
I saw this jacket in stores, but did not buy it. i went home, but could not get this trendy jacket out of my head. i returned to the store the very next day and purchased this item. i am very pleased with the quality and think that the fit could go well on most body types. i got the denim color, which may not be the best color for all, but i love it because denim is timeless and very versatile. i would suggest this to anyone and could not be happier with this! go out and buy it now, you won't re

Sentiment Output:
 ```json
{
  "sentiment": "positive"
}
``` 

This is the actual review:
I saw this jacket in stores, but did not buy it. i went home, but could not get this trendy jacket out of my head. i returned to the store the very next day and purchased this item. i am very pleased with the quality and think that the fit could go well on most body types. i got the denim color, which may not be the best color for all, but i love it because denim is timeless and very versatile. i would suggest this to anyone and could not be happier with this! go out and buy it now, you won't re

Summary Output:
 ```json
{
  "summary": "The reviewer initially hesitated but returned to purchase the trendy jacket, expressing satisfaction with its quality and fit, and recommending it to others for its timeless denim color."
}
``` 

This is the actual review:
I saw this jacket in stores, but did not buy it. i went home, but could not get this trendy jacket out of my head. i returned to the store the very next day and purchased this item. i am very pleased with the quality and think that the fit could go well on most body types. i got the denim color, which may not be the best color for all, but i love it because denim is timeless and very versatile. i would suggest this to anyone and could not be happier with this! go out and buy it now, you won't re

Personalized Message Output:
 ```json
{
  "personalized_message": "Thank you for sharing your experience! We're delighted to hear that you couldn't resist the trendy jacket and that you're pleased with its quality and fit. The denim color is indeed a classic choice, and we're 

This is the actual review:
I saw this jacket in stores, but did not buy it. i went home, but could not get this trendy jacket out of my head. i returned to the store the very next day and purchased this item. i am very pleased with the quality and think that the fit could go well on most body types. i got the denim color, which may not be the best color for all, but i love it because denim is timeless and very versatile. i would suggest this to anyone and could not be happier with this! go out and buy it now, you won't re

Retail Insight Output:
 ```json
{
  "retail_insight": {
    "suggestions": [
      {
        "highlight_features": "Emphasize the softness and stylish design of the jackets in marketing materials, as these attributes resonate well with customers." 


----- Processing Review 45 (Original Index: 2493) -----
This is the actual review:
I am 5'8" 140 lbs., 34b, and i ordered the 6 in denim. the dress is certainly tts: the 6 fit like a glove but wasn't at all tight. perhaps my expectations were too high with this dress--based on the other reviews, i thought that it would be perfect for my pear-shaped figure. however, i found that the a-line skirt accentuated my lower half, thereby making me look larger than i am, so i sent it back. otherwise, this is a great dress. it certainly did manage to hide lumps and bumps in spite of bein

Categories Output:
 ```json
{
  "Categories": {
    "Division": "General Petite",
    "Department": "Dresses",
    "Class": "Dresses",
    "My Category": "Possible Female Shopper"
  },
  "Review": { 

This is the actual review:
I am 5'8" 140 lbs., 34b, and i ordered the 6 in denim. the dress is certainly tts: the 6 fit like a glove but wasn't at all tight. perhaps my expectations were too high with this dress--based on the other reviews, i thought that it would be perfect for my pear-shaped figure. however, i found that the a-line skirt accentuated my lower half, thereby making me look larger than i am, so i sent it back. otherwise, this is a great dress. it certainly did manage to hide lumps and bumps in spite of bein

Sentiment Output:
 ```json
{
  "sentiment": "positive"
}
``` 

This is the actual review:
I am 5'8" 140 lbs., 34b, and i ordered the 6 in denim. the dress is certainly tts: the 6 fit like a glove but wasn't at all tight. perhaps my expectations were too high with this dress--based on the other reviews, i thought that it would be perfect for my pear-shaped figure. however, i found that the a-line skirt accentuated my lower half, thereby making me look larger than i am, so i sent it back. otherwise, this is a great dress. it certainly did manage to hide lumps and bumps in spite of bein

Summary Output:
 ```json
{
  "summary": "The reviewer found the dress to fit true to size and appreciated its ability to hide imperfections, but ultimately returned it due to the A-line skirt accentuating their pear-shaped figure."
}
``` 

This is the actual review:
I am 5'8" 140 lbs., 34b, and i ordered the 6 in denim. the dress is certainly tts: the 6 fit like a glove but wasn't at all tight. perhaps my expectations were too high with this dress--based on the other reviews, i thought that it would be perfect for my pear-shaped figure. however, i found that the a-line skirt accentuated my lower half, thereby making me look larger than i am, so i sent it back. otherwise, this is a great dress. it certainly did manage to hide lumps and bumps in spite of bein

Personalized Message Output:
 ```json
{
  "personalized_message": "Thank you for sharing your detailed feedback! We're glad to hear that the dress fit well and helped conceal any lumps and bumps. We understand that finding the perfect fit for your body shape can be challenging 

This is the actual review:
I am 5'8" 140 lbs., 34b, and i ordered the 6 in denim. the dress is certainly tts: the 6 fit like a glove but wasn't at all tight. perhaps my expectations were too high with this dress--based on the other reviews, i thought that it would be perfect for my pear-shaped figure. however, i found that the a-line skirt accentuated my lower half, thereby making me look larger than i am, so i sent it back. otherwise, this is a great dress. it certainly did manage to hide lumps and bumps in spite of bein

Retail Insight Output:
 ```json
{
  "retail_insight": {
    "suggestions": [
      {
        "highlight_features": "Consider emphasizing the softness and stylish design of the dresses in marketing materials, as these attributes resonate well with customers."
      }, 


----- Processing Review 46 (Original Index: 17303) -----
This is the actual review:
This dress is so beautiful. i am 50 5'8" and smaller on top larger on bottom. size 6 is perfect! was worried that it would hit me in all the wrong places. happy to say it does not. wore it for the first time yesterday and no lie i received no less than 15 compliments from strangers. just walked up to tell me how great the dress was. colors are so soft and beautiful. would buy in other colors if they had it. i can see myself wearing it to a casual dinner, to a wedding our just to feel special on

Categories Output:
 ```json
{
  "Categories": {
    "Division": "General",
    "Department": "Dresses",
    "Class": "Dresses",
    "My Category": "Possible Female Shopper"
  },
  "Review": { 

This is the actual review:
This dress is so beautiful. i am 50 5'8" and smaller on top larger on bottom. size 6 is perfect! was worried that it would hit me in all the wrong places. happy to say it does not. wore it for the first time yesterday and no lie i received no less than 15 compliments from strangers. just walked up to tell me how great the dress was. colors are so soft and beautiful. would buy in other colors if they had it. i can see myself wearing it to a casual dinner, to a wedding our just to feel special on

Sentiment Output:
 ```json
{
  "sentiment": "positive"
}
``` 

This is the actual review:
This dress is so beautiful. i am 50 5'8" and smaller on top larger on bottom. size 6 is perfect! was worried that it would hit me in all the wrong places. happy to say it does not. wore it for the first time yesterday and no lie i received no less than 15 compliments from strangers. just walked up to tell me how great the dress was. colors are so soft and beautiful. would buy in other colors if they had it. i can see myself wearing it to a casual dinner, to a wedding our just to feel special on

Summary Output:
 ```json
{
  "summary": "The reviewer finds the dress beautiful and perfectly fitting, receiving numerous compliments while appreciating its soft colors and versatility for various occasions."
}
``` 

This is the actual review:
This dress is so beautiful. i am 50 5'8" and smaller on top larger on bottom. size 6 is perfect! was worried that it would hit me in all the wrong places. happy to say it does not. wore it for the first time yesterday and no lie i received no less than 15 compliments from strangers. just walked up to tell me how great the dress was. colors are so soft and beautiful. would buy in other colors if they had it. i can see myself wearing it to a casual dinner, to a wedding our just to feel special on

Personalized Message Output:
 ```json
{
  "personalized_message": "Thank you for your fantastic review! We're delighted to hear that the dress fits you perfectly and that you received so many compliments. It's wonderful to know that you feel special wearing it. We appreciate your 

This is the actual review:
This dress is so beautiful. i am 50 5'8" and smaller on top larger on bottom. size 6 is perfect! was worried that it would hit me in all the wrong places. happy to say it does not. wore it for the first time yesterday and no lie i received no less than 15 compliments from strangers. just walked up to tell me how great the dress was. colors are so soft and beautiful. would buy in other colors if they had it. i can see myself wearing it to a casual dinner, to a wedding our just to feel special on

Retail Insight Output:
 ```json
{
  "retail_insight": {
    "suggestions": [
      {
        "highlight_features": "Emphasize the softness and stylish design of the dress in marketing materials, as these attributes resonate well with customers." 


----- Processing Review 47 (Original Index: 17762) -----
This is the actual review:
These are my favorite jeans. the fabric is soft but has a good amount of weight to them. all the details reflect a high quality jean. i usually have a very hard time finding jeans that fit my backside and thighs well without a huge gap at the waist. these sit lower on my hips, no gap, and plenty of space for my butt. they do run a bit large though. i have a 29" natural waist and think a 28 would have been even more perfect.

Categories Output:
 ```json
{
  "Categories": {
    "Division": "General Petite",
    "Department": "Bottoms",
    "Class": "Jeans",
    "My Category": "Possible Female Shopper"
  },
  "Review": { 

This is the actual review:
These are my favorite jeans. the fabric is soft but has a good amount of weight to them. all the details reflect a high quality jean. i usually have a very hard time finding jeans that fit my backside and thighs well without a huge gap at the waist. these sit lower on my hips, no gap, and plenty of space for my butt. they do run a bit large though. i have a 29" natural waist and think a 28 would have been even more perfect.

Sentiment Output:
 ```json
{
  "sentiment": "positive"
}
``` 

This is the actual review:
These are my favorite jeans. the fabric is soft but has a good amount of weight to them. all the details reflect a high quality jean. i usually have a very hard time finding jeans that fit my backside and thighs well without a huge gap at the waist. these sit lower on my hips, no gap, and plenty of space for my butt. they do run a bit large though. i have a 29" natural waist and think a 28 would have been even more perfect.

Summary Output:
 ```json
{
  "summary": "The reviewer loves these jeans for their soft yet substantial fabric and high-quality details. They fit well on the hips and thighs without gaps at the waist, although the reviewer suggests sizing down for a better fit." 

This is the actual review:
These are my favorite jeans. the fabric is soft but has a good amount of weight to them. all the details reflect a high quality jean. i usually have a very hard time finding jeans that fit my backside and thighs well without a huge gap at the waist. these sit lower on my hips, no gap, and plenty of space for my butt. they do run a bit large though. i have a 29" natural waist and think a 28 would have been even more perfect.

Personalized Message Output:
 ```json
{
  "personalized_message": "Thank you for your fantastic review! We're delighted to hear that you've found your favorite jeans with the perfect fit and quality. It's great to know they accommodate your shape so well. We appreciate your feedback 

This is the actual review:
These are my favorite jeans. the fabric is soft but has a good amount of weight to them. all the details reflect a high quality jean. i usually have a very hard time finding jeans that fit my backside and thighs well without a huge gap at the waist. these sit lower on my hips, no gap, and plenty of space for my butt. they do run a bit large though. i have a 29" natural waist and think a 28 would have been even more perfect.

Retail Insight Output:
 ```json
{
  "retail_insight": {
    "suggestions": [
      {
        "highlight_features": "Emphasize the softness and stylish design of the jeans in marketing materials, as these attributes resonate well with customers." 


----- Processing Review 48 (Original Index: 20425) -----
This is the actual review:
I was a bit hesitant to purchase because of the other reviews but so glad i did! i usually wear a 4-6 and the 6 fit great. it feels like high quality and i think the shirt will work well for both work and casual events. i have a long torso and don't find it too short at all. it is a tad tight across the shoulders, so if you are broad in that area, it might not work for you. overall, love!

Categories Output:
 ```json
{
  "Categories": {
    "Division": "General",
    "Department": "Tops",
    "Class": "Blouses",
    "My Category": "Possible Female Shopper"
  },
  "Review": { 

This is the actual review:
I was a bit hesitant to purchase because of the other reviews but so glad i did! i usually wear a 4-6 and the 6 fit great. it feels like high quality and i think the shirt will work well for both work and casual events. i have a long torso and don't find it too short at all. it is a tad tight across the shoulders, so if you are broad in that area, it might not work for you. overall, love!

Sentiment Output:
 ```json
{
  "sentiment": "positive",
  "review": {
    "division": "General",
    "department": "Tops",
    "class": "Blouses",
    "content": "I was a bit hesitant 

This is the actual review:
I was a bit hesitant to purchase because of the other reviews but so glad i did! i usually wear a 4-6 and the 6 fit great. it feels like high quality and i think the shirt will work well for both work and casual events. i have a long torso and don't find it too short at all. it is a tad tight across the shoulders, so if you are broad in that area, it might not work for you. overall, love!

Summary Output:
 ```json
{
  "summary": "The reviewer expresses initial hesitation but is pleased with the purchase, finding the fit great for a size 6 and appreciating the high quality. The blouse is versatile for both work and casual events, though it may 

This is the actual review:
I was a bit hesitant to purchase because of the other reviews but so glad i did! i usually wear a 4-6 and the 6 fit great. it feels like high quality and i think the shirt will work well for both work and casual events. i have a long torso and don't find it too short at all. it is a tad tight across the shoulders, so if you are broad in that area, it might not work for you. overall, love!

Personalized Message Output:
 ```json
{
  "personalized_message": "Thank you for sharing your experience! We're delighted to hear that you took the plunge and are happy with your purchase. It's great to know that the fit works well for you and that you find it 

This is the actual review:
I was a bit hesitant to purchase because of the other reviews but so glad i did! i usually wear a 4-6 and the 6 fit great. it feels like high quality and i think the shirt will work well for both work and casual events. i have a long torso and don't find it too short at all. it is a tad tight across the shoulders, so if you are broad in that area, it might not work for you. overall, love!

Retail Insight Output:
 ```json
{
  "retail_insight": {
    "suggestions": [
      "Highlight the softness and stylish design in marketing materials, as these features resonate well with customers.",
      "Showcase customer fit examples to help potential buyers make 


----- Processing Review 49 (Original Index: 9195) -----
This is the actual review:
I wanted to love this sweater. the lavender color and design with the pleated back was beautiful. however when i put it on, it added at least 20 pounds to my frame. unfortunately it's going back since the cut just doesn't work on my frame.

Categories Output:
 ```json
{
  "Categories": {
    "Division": "General",
    "Department": "Tops",
    "Class": "Fine gauge",
    "My Category": "Possible Female Shopper"
  },
  "Review": { 

This is the actual review:
I wanted to love this sweater. the lavender color and design with the pleated back was beautiful. however when i put it on, it added at least 20 pounds to my frame. unfortunately it's going back since the cut just doesn't work on my frame.

Sentiment Output:
 ```json
{
  "sentiment": "negative"
}
``` 

This is the actual review:
I wanted to love this sweater. the lavender color and design with the pleated back was beautiful. however when i put it on, it added at least 20 pounds to my frame. unfortunately it's going back since the cut just doesn't work on my frame.

Summary Output:
 ```json
{
  "summary": "The reviewer finds the lavender color and pleated back design beautiful but feels the sweater adds bulk to their frame, leading to a decision to return it."
}
``` 

This is the actual review:
I wanted to love this sweater. the lavender color and design with the pleated back was beautiful. however when i put it on, it added at least 20 pounds to my frame. unfortunately it's going back since the cut just doesn't work on my frame.

Personalized Message Output:
 ```json
{
  "personalized_message": "Thank you for your honest feedback! We're sorry to hear that the fit of the sweater didn't work for you, despite its beautiful color and design. We appreciate your input and hope you find a style 

This is the actual review:
I wanted to love this sweater. the lavender color and design with the pleated back was beautiful. however when i put it on, it added at least 20 pounds to my frame. unfortunately it's going back since the cut just doesn't work on my frame.

Retail Insight Output:
 ```json
{
  "retail_insight": {
    "suggestions": [
      {
        "highlight_features": "Consider emphasizing the softness and stylish design in marketing materials, as these features resonate well with customers."
      },
      { 


----- Processing Review 50 (Original Index: 13747) -----
This is the actual review:
Beautiful top but it didn't fit. i am small chested with wide shoulders, normally wear a xs or s - ordered the xs and it swallowed me.

Categories Output:
 ```json
{
  "Categories": {
    "Division": "General",
    "Department": "Tops",
    "Class": "Knits",
    "My Category": "Possible Female Shopper"
  },
  "Review": { 

This is the actual review:
Beautiful top but it didn't fit. i am small chested with wide shoulders, normally wear a xs or s - ordered the xs and it swallowed me.

Sentiment Output:
 ```json
{
  "sentiment": "negative"
}
``` 

This is the actual review:
Beautiful top but it didn't fit. i am small chested with wide shoulders, normally wear a xs or s - ordered the xs and it swallowed me.

Summary Output:
 ```json
{
  "summary": "The reviewer finds the top beautiful but notes that it did not fit properly, as the XS size was too large for their small chest and wide shoulders."
}
``` 

This is the actual review:
Beautiful top but it didn't fit. i am small chested with wide shoulders, normally wear a xs or s - ordered the xs and it swallowed me.

Personalized Message Output:
 ```json
{
  "personalized_message": "Thank you for your feedback! We're sorry to hear that the fit of the top didn't work for you. We appreciate your input and will take it into consideration for future sizing adjustments. If you have 

This is the actual review:
Beautiful top but it didn't fit. i am small chested with wide shoulders, normally wear a xs or s - ordered the xs and it swallowed me.

Retail Insight Output:
 ```json
{
  "retail_insight": {
    "suggestions": [
      {
        "highlight_features": "Consider highlighting the softness and stylish design in marketing materials, as these features resonate well with customers."
      },
      { 


--- Consolidated COT V1 Results (First 5 Rows) ---
   review_index                              categories_raw_output  \
0         11956  ```json\n{\n  "Categories": {\n    "Division":...   
1          8421  ```json\n{\n  "Categories": {\n    "Division":...   
2         12715  ```json\n{\n  "Categories": {\n    "Division":...   
3         18326  ```json\n{\n  "Categories": {\n    "Division":...   
4         17154  ```json\n{\n  "Categories": {\n    "Division":...   

                            sentiment_raw_output  \
0   ```json\n{\n  "sentiment": "neutral"\n}\n```   
1  ```json\n{\n  "sentiment": "positive"\n}\n```   
2   ```json\n{\n  "sentiment": "neutral"\n}\n```   
3  ```json\n{\n  "sentiment": "positive"\n}\n```   
4  ```json\n{\n  "sentiment": "positive"\n}\n```   

                                  summary_raw_output  \
0  ```json\n{\n  "summary": "The reviewer loves t...   
1  ```json\n{\n  "summary": "The reviewer is high...   
2  ```json\n{\n  "summary": "The reviewer express...   
3  ```json\n{\n  "summary": "The reviewer express...   
4  ```json\n{\n  "summary": "The reviewer is plea...   

                     personalized_message_raw_output  \
0  ```json\n{\n  "personalized_message": "Thank y...   
1  ```json\n{\n  "personalized_message": "Thank y...   
2  ```json\n{\n  "personalized_message": "Thank y...   
3  ```json\n{\n  "personalized_message": "Thank y...   
4  ```json\n{\n  "personalized_message": "Thank y...   

                           retail_insight_raw_output  \
0  ```json\n{\n  "retail_insight": {\n    "sugges...   
1  ```json\n{\n  "retail_insight": {\n    "sugges...   
2  ```json\n{\n  "retail_insight": {\n    "sugges...   
3  ```json\n{\n  "retail_insight": {\n    "sugges...   
4  ```json\n{\n  "retail_insight": {\n    "sugges...   

                           original_user_content_cot  
0  Division: General\nDepartment: Tops\nClass: Kn...  
1  Division: General\nDepartment: Tops\nClass: Bl...  
2  Division: General\nDepartment: Dresses\nClass:...  
3  Division: General Petite\nDepartment: Jackets\...  
4  Division: General Petite\nDepartment: Tops\nCl...

import json
import re
import pandas as pd

# Helper function to extract descriptive text for each category (reusing the one from zero-shot judge)
def extract_description_for_category(full_response_text, start_pattern, end_pattern):
    match = re.search(re.escape(start_pattern) + r'\s*\n(.*?)\n\s*' + re.escape(end_pattern), full_response_text, re.DOTALL)
    if match:
        description = match.group(1).strip()
        description = re.sub(r'\s*\*\*Rating\*\*:\s*\d+\.\d+', '', description).strip()
        return description
    return "Description not found"

# Define the patterns for each section (reusing from zero-shot judge)
patterns_to_extract = [
    ("Categories_Description", "1. **Categories**:", "2. **Sentiment**:"),
    ("Sentiment_Description", "2. **Sentiment**:", "3. **Summary**:"),
    ("Summary_Description", "3. **Summary**:", "4. **Personalized Message**:"),
    ("Personalized_Message_Description", "4. **Personalized Message**:", "5. **Retail Insight**: "), # Adjusted end pattern slightly
    ("Retail_Insight_Description", "5. **Retail Insight**:", "Overall Evaluation:"),
]

all_cot_eval_results = []
all_cot_descriptive_results = []

for i, row in cot_results_df.iterrows():
    original_user_content_for_judge = row['original_user_content_cot']

    # Evaluate Categories
    eval_categories = llm_as_judge(
        llm_output_to_judge=row['categories_raw_output'],
        original_user_prompt_string_from_zero_shot=original_user_content_for_judge
    )

    # Evaluate Sentiment
    eval_sentiment = llm_as_judge(
        llm_output_to_judge=row['sentiment_raw_output'],
        original_user_prompt_string_from_zero_shot=original_user_content_for_judge
    )

    # Evaluate Summary
    eval_summary = llm_as_judge(
        llm_output_to_judge=row['summary_raw_output'],
        original_user_prompt_string_from_zero_shot=original_user_content_for_judge
    )

    # Evaluate Personalized Message
    eval_pm = llm_as_judge(
        llm_output_to_judge=row['personalized_message_raw_output'],
        original_user_prompt_string_from_zero_shot=original_user_content_for_judge
    )

    # Evaluate Retail Insight
    eval_ri = llm_as_judge(
        llm_output_to_judge=row['retail_insight_raw_output'],
        original_user_prompt_string_from_zero_shot=original_user_content_for_judge
    )

    # Collect numerical scores and descriptive results for this review
    # Categories
    match_scores_cat = re.search(r'```json\n(.*?)```', eval_categories['judge_response'], re.DOTALL)
    scores_cat = json.loads(match_scores_cat.group(1)) if match_scores_cat else {}
    extracted_desc_cat = {name: extract_description_for_category(eval_categories['judge_response'], start_pat, end_pat) for name, start_pat, end_pat in patterns_to_extract}

    # Sentiment
    match_scores_sent = re.search(r'```json\n(.*?)```', eval_sentiment['judge_response'], re.DOTALL)
    scores_sent = json.loads(match_scores_sent.group(1)) if match_scores_sent else {}
    extracted_desc_sent = {name: extract_description_for_category(eval_sentiment['judge_response'], start_pat, end_pat) for name, start_pat, end_pat in patterns_to_extract}

    # Summary
    match_scores_sum = re.search(r'```json\n(.*?)```', eval_summary['judge_response'], re.DOTALL)
    scores_sum = json.loads(match_scores_sum.group(1)) if match_scores_sum else {}
    extracted_desc_sum = {name: extract_description_for_category(eval_summary['judge_response'], start_pat, end_pat) for name, start_pat, end_pat in patterns_to_extract}

    # Personalized Message
    match_scores_pm = re.search(r'```json\n(.*?)```', eval_pm['judge_response'], re.DOTALL)
    scores_pm = json.loads(match_scores_pm.group(1)) if match_scores_pm else {}
    extracted_desc_pm = {name: extract_description_for_category(eval_pm['judge_response'], start_pat, end_pat) for name, start_pat, end_pat in patterns_to_extract}

    # Retail Insight
    match_scores_ri = re.search(r'```json\n(.*?)```', eval_ri['judge_response'], re.DOTALL)
    scores_ri = json.loads(match_scores_ri.group(1)) if match_scores_ri else {}
    extracted_desc_ri = {name: extract_description_for_category(eval_ri['judge_response'], start_pat, end_pat) for name, start_pat, end_pat in patterns_to_extract}

    all_cot_eval_results.append({
        'review_index': row['review_index'],
        'categories_score': scores_cat.get('categories'),
        'sentiment_score': scores_sent.get('sentiment'),
        'summary_score': scores_sum.get('summary'),
        'personalized_message_score': scores_pm.get('personalized_message'),
        'retail_insight_score': scores_ri.get('retail_insight'),
    })

    # Collect descriptive reasons (e.g., from each component's evaluation)
    all_cot_descriptive_results.append({
        'review_index': row['review_index'],
        'categories_desc': extracted_desc_cat.get('Categories_Description'),
        'sentiment_desc': extracted_desc_sent.get('Sentiment_Description'),
        'summary_desc': extracted_desc_sum.get('Summary_Description'),
        'personalized_message_desc': extracted_desc_pm.get('Personalized_Message_Description'),
        'retail_insight_desc': extracted_desc_ri.get('Retail_Insight_Description'),
    })

cot_numerical_eval_df = pd.DataFrame(all_cot_eval_results)
cot_descriptive_eval_df = pd.DataFrame(all_cot_descriptive_results)

print("\n--- CoT Numerical Evaluation (Head) ---")
display(cot_numerical_eval_df.head())
print("\nAverage CoT Numerical Scores:")
print(cot_numerical_eval_df.mean(numeric_only=True))

print("\n--- CoT Descriptive Evaluation (Head) ---")
display(cot_descriptive_eval_df.head())

--- CoT Numerical Evaluation (Head) ---

Average CoT Numerical Scores:
review_index                  11993.040
categories_score                  0.785
sentiment_score                   0.861
summary_score                     0.831
personalized_message_score        0.712
retail_insight_score              0.597
dtype: float64

--- CoT Descriptive Evaluation (Head) ---

print("\n--- CoT Numerical Evaluation (Head) ---")
display(cot_numerical_eval_df.head(20))
print("\nAverage CoT Numerical Scores:")
print(cot_numerical_eval_df.mean(numeric_only=True))

print("\n--- CoT Descriptive Evaluation (Head) ---")
display(cot_descriptive_eval_df.head(20))

--- CoT Numerical Evaluation (Head) ---

Average CoT Numerical Scores:
review_index                  11993.040
categories_score                  0.785
sentiment_score                   0.861
summary_score                     0.831
personalized_message_score        0.712
retail_insight_score              0.597
dtype: float64

--- CoT Descriptive Evaluation (Head) ---

reviews5 = reviews4.copy()
reviews5['Recom'] = reviews5['Recommended.IND']
reviews5 = reviews5.drop(columns=['Recommended.IND'])
reviews5.head()

reviews5.describe()

reviews5.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 22628 entries, 0 to 22627
Data columns (total 11 columns):
 #   Column                   Non-Null Count  Dtype 
---  ------                   --------------  ----- 
 0   index                    22628 non-null  int64 
 1   Clothing.ID              22628 non-null  int64 
 2   Age                      22628 non-null  int64 
 3   Title                    22628 non-null  object
 4   Review.Text              22628 non-null  object
 5   Rating                   22628 non-null  int64 
 6   Positive.Feedback.Count  22628 non-null  int64 
 7   Division.Name            22628 non-null  object
 8   Department.Name          22628 non-null  object
 9   Class.Name               22628 non-null  object
 10  Recom                    22628 non-null  int64 
dtypes: int64(6), object(5)
memory usage: 1.9+ MB

def recom_input_one5():
    """
    Samples one review and returns its
    """
    one_sample5 = reviews5.sample(n=1) # Get a single random row
    return {
        "recom_num": one_sample5['Recom'],
        "review_text": one_sample5['Review.Text'],
    }

SYSTEM_MESSAGE_CS5 = """
You are a review analyst. For each review output either a 0 or a 1, and use the
review to compose a short reason for the score.
"""

BASE_MESSAGES_CS5 = [
    {"role": "system", "content": SYSTEM_MESSAGE_CS5}
]

def review_prompt_one5(review_data5):
    """
    Samples one review and returns its recommendation number,
    and Review.Text as a dictionary.
    """

    # The previous if statement for `reveiw_data5 is None` had a typo and is commented out.
    # Ensure review_data5 is always passed correctly from recom_input_one5().

    user_content5 = (
        f"Review: {review_data5['review_text']}"
        # Removed Recommendation: {review_data5['recom_num']} as the model is supposed to predict it
    )

    instructions = BASE_MESSAGES_CS5 + [{
        "role": "user",
        "content": user_content5
    }]

    response = client.chat.completions.create(
        model="gpt-4o-mini", # Changed model name to gpt-4o-mini
        messages=instructions,
        temperature=0.0,
        max_tokens=150 # Increased max_tokens to allow for the reason
    )
    raw = response.choices[0].message.content
    print(raw)

    return raw

#review_prompt_one5(recom_input_one5())

import re
import pandas as pd # Ensure pandas is imported if not already
import numpy as np  # Ensure numpy is imported if not already
from tqdm import tqdm # Import tqdm for progress bar

def parser_recommendation(raw_text):
   s = str(raw_text).strip()
   # Regex to find 'Score: X' or 'Recommended: X'
   m = re.search(r'(?:Score|Recommended)\s*:\s*([01])\b', s, re.IGNORECASE)
   flag = int(m.group(1)) if m else np.nan
   # Regex to find 'Reason: ...'
   m3 = re.search(r'Reason\s*:\s*(.+)', s, re.IGNORECASE)
   reason = m3.group(1).strip() if m3 else ""
   return (flag, reason[:200], s)

# --- Start of modified section ---
# Decide on a sample size (as discussed in previous text cells for budget control)
sample_size = 10 #`50
sampled_reviews = reviews5.sample(n=sample_size, random_state=42).reset_index(drop=False) # Keep original index and reset for iteration

all_llm_predictions = []

for idx, row in tqdm(sampled_reviews.iterrows(), total=sample_size, desc="Generating LLM predictions"):
    # Prepare data for review_prompt_one5 from the current row
    review_data_for_llm = {
        "review_text": row['Review.Text'],
        "recom_num": row['Recom'] # Included for context/potential future use, not passed to LLM
    }

    # Call review_prompt_one5 to get raw LLM output for the current review
    raw_llm_output = review_prompt_one5(review_data_for_llm) # Fixed the missing parenthesis

    # Parse the LLM's raw output
    flag, reason, raw_clean = parser_recommendation(raw_llm_output)

    # Store the prediction along with relevant original data for evaluation
    all_llm_predictions.append({
        "Original_Index": row['index'],
        "Clothing.ID": row['Clothing.ID'],
        "Age": row['Age'],
        "Title": row['Title'],
        "Review.Text": row['Review.Text'],
        "Rating": row['Rating'],
        "Positive.Feedback.Count": row['Positive.Feedback.Count'],
        "Division.Name": row['Division.Name'],
        "Department.Name": row['Department.Name'],
        "Class.Name": row['Class.Name'],
        "Recom": row['Recom'], # The actual recommendation
        "LLM_Recommended_Raw": raw_clean,
        "LLM_Recommended_Flag": flag,
        "LLM_Recommend_Reason": reason
    })

# Create a DataFrame for the LLM's predictions after the loop
llm_pred_df = pd.DataFrame(all_llm_predictions)

# The recom_one DataFrame should now contain all relevant data including LLM predictions for the sample
recom_one = llm_pred_df.copy() # Make recom_one the full prediction table for the sample

# --- End of modified section ---
recom_one.head()

Generating LLM predictions:   2%|▏         | 1/50 [00:01<01:27,  1.79s/it]

Score: 0  
Reason: The reviewer expresses strong dissatisfaction with the dress's armpit design, indicating that it detracts significantly from their overall enjoyment of the product, leading to a negative assessment.

Generating LLM predictions:   4%|▍         | 2/50 [00:03<01:20,  1.68s/it]

Score: 0  
Reason: The reviewer expresses disappointment with the sweater due to its sheer fabric, fit issues, and color suitability, indicating an overall negative experience.

Generating LLM predictions:   6%|▌         | 3/50 [00:05<01:25,  1.82s/it]

Score: 1  
Reason: The reviewer expresses strong satisfaction with the tee, highlighting its softness, flattering fit, and suitability for their body type, indicating a positive experience.

Generating LLM predictions:   8%|▊         | 4/50 [00:07<01:28,  1.93s/it]

Score: 0  
Reason: The reviewer appreciated the style of the swimsuit but was disappointed by the lack of support in the chest area, leading to a return.

Generating LLM predictions:  10%|█         | 5/50 [00:08<01:16,  1.71s/it]

Score: 0  
Reason: The reviewer experienced significant sizing issues and found the fit unflattering, leading to disappointment despite the item's aesthetic appeal.

recom_one.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 50 entries, 0 to 49
Data columns (total 14 columns):
 #   Column                   Non-Null Count  Dtype 
---  ------                   --------------  ----- 
 0   Original_Index           50 non-null     int64 
 1   Clothing.ID              50 non-null     int64 
 2   Age                      50 non-null     int64 
 3   Title                    50 non-null     object
 4   Review.Text              50 non-null     object
 5   Rating                   50 non-null     int64 
 6   Positive.Feedback.Count  50 non-null     int64 
 7   Division.Name            50 non-null     object
 8   Department.Name          50 non-null     object
 9   Class.Name               50 non-null     object
 10  Recom                    50 non-null     int64 
 11  LLM_Recommended_Raw      50 non-null     object
 12  LLM_Recommended_Flag     50 non-null     int64 
 13  LLM_Recommend_Reason     50 non-null     object
dtypes: int64(7), object(7)
memory usage: 5.6+ KB

recom_one['Actual_Recommendation'] = recom_one['Recom']

recom_one.head()

from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
import matplotlib.pyplot as plt
import seaborn as sns

accu_rec = recom_one['Actual_Recommendation']
pred_rec = recom_one['LLM_Recommended_Flag']
accuracy = accuracy_score(accu_rec, pred_rec)
print(f"Accuracy: {accuracy}")

cm = confusion_matrix(accu_rec, pred_rec)
print(f"Confusion Matrix:\n{cm}")

# Corrected line to plot confusion matrix using seaborn.heatmap
sns.heatmap(cm, annot=True, fmt='d', cmap='Blues')
plt.title('Confusion Matrix')
plt.xlabel('Predicted')
plt.ylabel('Actual')
plt.show()

report = classification_report(accu_rec, pred_rec)
print(f"Classification Report:\n{report}")

Accuracy: 0.92
Confusion Matrix:
[[11  0]
 [ 4 35]]

Classification Report:
              precision    recall  f1-score   support

           0       0.73      1.00      0.85        11
           1       1.00      0.90      0.95        39

    accuracy                           0.92        50
   macro avg       0.87      0.95      0.90        50
weighted avg       0.94      0.92      0.92        50

SYSTEM_MESSAGE_ECS0 = """
You are a review analyst. For the Actaul Recommendation not equal to the LLM Recommendation,
use the original review Review.Text and the LLM_Recommend_Reason to give an
explanation for why the model’s decision may have differed from the human label.
"""

BASE_MESSAGES_ECS0 = [
    {"role": "system", "content": SYSTEM_MESSAGE_ECS0}
]

def explain_prompt5(review_row):
    """
    Generates an explanation for why the LLM's recommendation might differ from the actual recommendation.
    Expects a pandas Series (a row) containing relevant review data.
    """

    user_content_prompt = (
        f"Actual Recommendation: {review_row['Actual_Recommendation']}\n"
        f"LLM Predicted Flag: {review_row['LLM_Recommended_Flag']}\n"
        f"LLM Predicted Reason: {review_row['LLM_Recommend_Reason']}\n"
        f"Original Review Text: {review_row['Review.Text']}\n\n"
        "Explain why the LLM's prediction might differ from the actual recommendation based on the provided information."
    )

    instructions = BASE_MESSAGES_ECS0 + [{
        "role": "user",
        "content": user_content_prompt
    }]

    response = client.chat.completions.create(
        model="gpt-4o-mini",
        messages=instructions,
        temperature=0.0,
        max_tokens=200 # Adjusted max_tokens for a full explanation
    )
    raw_expl = response.choices[0].message.content
    print(raw_expl)
    return raw_expl

sample_size2 = 10 # This variable is currently unused with the iteration over recom_one

llm_explanations = []

# Iterate over rows in recom_one DataFrame
for indx, row in tqdm(recom_one.iterrows(), total=len(recom_one), desc="Explaining Mismatched LLM predictions"):
    # Check if Actual Recommendation is not equal to LLM Recommended Flag
    if row['Actual_Recommendation'] != row['LLM_Recommended_Flag']:
        print(f"--- Mismatch found for Review Index: {row['Original_Index']} ---")
        raw_llm_expl = explain_prompt5(row) # Pass the entire row (pandas Series) to the function
        llm_explanations.append({
            "Original_Index": row['Original_Index'],
            "Actual_Recommendation": row['Actual_Recommendation'],
            "LLM_Recommended_Flag": row['LLM_Recommended_Flag'],
            "LLM_Recommended_Reason": row['LLM_Recommend_Reason'],
            "Explanation_of_Mismatch": raw_llm_expl
        })

# Convert the list of explanations to a DataFrame
if llm_explanations:
    llm_explanations_df = pd.DataFrame(llm_explanations)
    display(llm_explanations_df.head())
else:
    print("No mismatches found to explain.")

Explaining Mismatched LLM predictions:   0%|          | 0/50 [00:00<?, ?it/s]

--- Mismatch found for Review Index: 8037 ---

Explaining Mismatched LLM predictions:   2%|▏         | 1/50 [00:05<04:46,  5.84s/it]

The LLM's prediction of a flag (0) suggests that it interpreted the review as generally positive, focusing on the reviewer’s love for the dress and their admiration for its style, likening it to Audrey Hepburn. However, the reviewer explicitly states their dissatisfaction with the armpit design, which they believe detracts from the overall enjoyment of the dress. This strong negative sentiment regarding a specific aspect of the product is significant enough to lead to an overall negative recommendation (1).

The discrepancy arises because the LLM may have placed too much emphasis on the positive aspects mentioned at the beginning of the review, overlooking the critical feedback that ultimately influenced the reviewer’s final recommendation. The reviewer’s desire for a redesign of the armpits indicates a serious concern that affects their overall assessment, which the LLM did not fully capture in its reasoning. Thus, the LLM's prediction may have failed to adequately weigh the negative impact of the armpit design on the reviewer's overall experience with the dress
--- Mismatch found for Review Index: 11051 ---

Explaining Mismatched LLM predictions:   8%|▊         | 4/50 [00:10<01:46,  2.32s/it]

The LLM's prediction of a flag (0) suggests that it interpreted the review as generally positive, focusing on the appreciation of the swimsuit's style. However, the actual recommendation (1) indicates a negative experience due to the lack of support in the chest area, which ultimately led to the return of the product. 

The discrepancy may arise from the LLM's emphasis on the positive aspects mentioned in the review, such as the style being loved, while underestimating the significance of the critical issue regarding support. The reviewer clearly states that the lack of support was a major disappointment, which directly influenced their decision to return the swimsuit. The LLM may have failed to fully weigh the impact of this negative aspect, leading to a more favorable prediction than warranted by the overall sentiment of the review.
--- Mismatch found for Review Index: 4821 ---

Explaining Mismatched LLM predictions:  70%|███████   | 35/50 [00:15<00:05,  2.98it/s]

The LLM's prediction of a flag of 0 suggests that it did not identify the review as negative, while the actual recommendation of 1 indicates that it was indeed considered negative. The discrepancy may stem from the LLM's interpretation of the review's tone and content. 

In the original review, the reviewer mentions that "the shirt was cute," which could be interpreted as a positive remark. However, the key issue lies in the dissatisfaction expressed regarding the fit of the shirt, particularly for someone who is petite. The LLM may have focused on the positive aspect of the shirt's appearance and overlooked the significant concern about the fit, which ultimately led to the reviewer's dissatisfaction.

The LLM's reasoning might not have fully accounted for the impact of the fit issue on the overall experience of the reviewer. While the reviewer acknowledges a positive attribute (the cuteness of the shirt), the primary concern about the fit being unsuitable for their body type is a critical factor that should have led to a negative
--- Mismatch found for Review Index: 21198 ---

Explaining Mismatched LLM predictions: 100%|██████████| 50/50 [00:23<00:00,  2.10it/s]

The LLM's prediction of a flag (0) suggests that it did not identify the review as negative, likely focusing on the positive aspect of the material that the reviewer mentioned. However, the actual recommendation (1) indicates that the review should be flagged as negative due to the reviewer’s dissatisfaction with the fit of the product, which ultimately led to a return. 

The discrepancy arises because the LLM may have placed more weight on the positive comment about the material, overlooking the significant negative experience related to the fit. The reviewer explicitly states that the fit is "not for me," which is a clear indication of dissatisfaction, and the decision to return the items reinforces this negative sentiment. Thus, while the LLM recognized a positive element, it failed to fully account for the overall negative experience expressed by the reviewer, leading to a different recommendation.

import matplotlib.pyplot as plt
import seaborn as sns
import json
import re
import pandas as pd

def extract_sentiment_from_raw_output(raw_output_str):
    """
    Extracts sentiment (positive, neutral, negative) from raw LLM output strings.
    Handles JSON objects with a 'sentiment' key or direct keyword presence.
    """
    if not isinstance(raw_output_str, str):
        return None

    # First, try to extract the inner JSON string if wrapped in ```json\n...\n```
    match_json_block = re.search(r'```json\n(.*)```', raw_output_str, re.DOTALL)
    json_str_to_parse = match_json_block.group(1).strip() if match_json_block else raw_output_str

    try:
        parsed_data = json.loads(json_str_to_parse)
        if isinstance(parsed_data, dict) and 'sentiment' in parsed_data:
            return str(parsed_data['sentiment']).lower()
    except json.JSONDecodeError:
        pass # Not a JSON, or malformed JSON, proceed to keyword search

    # Fallback to keyword search for sentiments if not a clean JSON or key not found
    sentiment_keywords = ['positive', 'neutral', 'negative']
    for keyword in sentiment_keywords:
        if keyword in raw_output_str.lower():
            return keyword

    return None # No recognizable sentiment found

# --- Apply sentiment extraction to all relevant DataFrames ---

# Zero-Shot V1: Already has 'extracted_sentiment' from cell 1ff8af5f
# zero_shot_results_df['extracted_sentiment']

# Few-Shot V1: Already has 'extracted_sentiment' from cell HhA7w9hLcDeE
# few_shot_results_df1['extracted_sentiment']

# Zero-Shot V2
zero_shot_results_df_v2['extracted_sentiment'] = zero_shot_results_df_v2['raw_llm_output'].apply(extract_sentiment_from_raw_output)

# Few-Shot V2
few_shot_results_df_v2['extracted_sentiment'] = few_shot_results_df_v2['raw_llm_output'].apply(extract_sentiment_from_raw_output)

# CoT V1
cot_results_df['extracted_sentiment'] = cot_results_df['sentiment_raw_output'].apply(extract_sentiment_from_raw_output)

# CoT V2
cot_results_df_v2['extracted_sentiment'] = cot_results_df_v2['sentiment_raw_output'].apply(extract_sentiment_from_raw_output)

# --- Consolidate sentiment data for plotting ---

sentiment_counts = {
    'Zero-Shot V1': zero_shot_results_df['extracted_sentiment'].value_counts(normalize=True).reindex(['positive', 'neutral', 'negative'], fill_value=0),
    'Zero-Shot V2': zero_shot_results_df_v2['extracted_sentiment'].value_counts(normalize=True).reindex(['positive', 'neutral', 'negative'], fill_value=0),
    'Few-Shot V1': few_shot_results_df1['extracted_sentiment'].value_counts(normalize=True).reindex(['positive', 'neutral', 'negative'], fill_value=0),
    'Few-Shot V2': few_shot_results_df_v2['extracted_sentiment'].value_counts(normalize=True).reindex(['positive', 'neutral', 'negative'], fill_value=0),
    'CoT V1': cot_results_df['extracted_sentiment'].value_counts(normalize=True).reindex(['positive', 'neutral', 'negative'], fill_value=0),
    'CoT V2': cot_results_df_v2['extracted_sentiment'].value_counts(normalize=True).reindex(['positive', 'neutral', 'negative'], fill_value=0),
}

sentiment_comparison_df = pd.DataFrame(sentiment_counts).T

print("\n--- Sentiment Distribution Across All Versions ---")
display(sentiment_comparison_df)

# --- Plotting the comparison ---

sentiment_comparison_df_long = sentiment_comparison_df.reset_index().melt(id_vars='index', var_name='Sentiment', value_name='Proportion')
sentiment_comparison_df_long.rename(columns={'index': 'Technique_Version'}, inplace=True)

plt.figure(figsize=(14, 8))
sns.barplot(x='Technique_Version', y='Proportion', hue='Sentiment', data=sentiment_comparison_df_long, palette='Paired')
plt.title('Normalized Sentiment Distribution by Prompting Technique and Version')
plt.ylabel('Proportion of Reviews')
plt.xlabel('Prompting Technique and Version')
plt.xticks(rotation=45, ha='right')
plt.legend(title='Sentiment')
plt.grid(axis='y', linestyle='--', alpha=0.7)
plt.tight_layout()
plt.show()

--- Sentiment Distribution Across All Versions ---

from textblob import TextBlob
import matplotlib.pyplot as plt
import seaborn as sns
import json
import re
import pandas as pd

def extract_retail_insight(raw_output_str):
    """
    Extracts the retail_insight from raw LLM output strings, handling JSON structures.
    """
    if not isinstance(raw_output_str, str):
        return ""

    # Try to parse as a direct JSON object (common for CoT components)
    try:
        parsed_data = json.loads(raw_output_str)
        if isinstance(parsed_data, dict) and 'retail_insight' in parsed_data:
            return parsed_data['retail_insight']
    except json.JSONDecodeError:
        pass # Not a direct JSON, try wrapped JSON

    # Try to extract JSON wrapped in ```json\n...\n``` (common for Zero-Shot/Few-Shot V1/V2)
    match_json_block = re.search(r'```json\n(.*)```', raw_output_str, re.DOTALL)
    if match_json_block:
        json_str_to_parse = match_json_block.group(1).strip()
        try:
            parsed_data = json.loads(json_str_to_parse)
            if isinstance(parsed_data, dict) and 'retail_insight' in parsed_data:
                return parsed_data['retail_insight']
        except json.JSONDecodeError:
            pass # Malformed wrapped JSON

    return "" # Return empty string if no insight found


# --- Apply retail insight extraction and TextBlob sentiment analysis ---

insight_polarities = {}

# Zero-Shot V1
zero_shot_results_df['retail_insight_text'] = zero_shot_results_df['raw_llm_output'].apply(extract_retail_insight)
zero_shot_results_df['retail_insight_polarity'] = zero_shot_results_df['retail_insight_text'].apply(lambda x: TextBlob(str(x)).sentiment.polarity)
insight_polarities['Zero-Shot V1'] = zero_shot_results_df['retail_insight_polarity'].mean()

# Zero-Shot V2
zero_shot_results_df_v2['retail_insight_text'] = zero_shot_results_df_v2['raw_llm_output'].apply(extract_retail_insight)
zero_shot_results_df_v2['retail_insight_polarity'] = zero_shot_results_df_v2['retail_insight_text'].apply(lambda x: TextBlob(str(x)).sentiment.polarity)
insight_polarities['Zero-Shot V2'] = zero_shot_results_df_v2['retail_insight_polarity'].mean()

# Few-Shot V1
few_shot_results_df1['retail_insight_text'] = few_shot_results_df1['raw_llm_output1'].apply(extract_retail_insight)
few_shot_results_df1['retail_insight_polarity'] = few_shot_results_df1['retail_insight_text'].apply(lambda x: TextBlob(str(x)).sentiment.polarity)
insight_polarities['Few-Shot V1'] = few_shot_results_df1['retail_insight_polarity'].mean()

# Few-Shot V2
few_shot_results_df_v2['retail_insight_text'] = few_shot_results_df_v2['raw_llm_output'].apply(extract_retail_insight)
few_shot_results_df_v2['retail_insight_polarity'] = few_shot_results_df_v2['retail_insight_text'].apply(lambda x: TextBlob(str(x)).sentiment.polarity)
insight_polarities['Few-Shot V2'] = few_shot_results_df_v2['retail_insight_polarity'].mean()

# CoT V1
cot_results_df['retail_insight_text'] = cot_results_df['retail_insight_raw_output'].apply(extract_retail_insight)
cot_results_df['retail_insight_polarity'] = cot_results_df['retail_insight_text'].apply(lambda x: TextBlob(str(x)).sentiment.polarity)
insight_polarities['CoT V1'] = cot_results_df['retail_insight_polarity'].mean()

# CoT V2
cot_results_df_v2['retail_insight_text'] = cot_results_df_v2['retail_insight_raw_output'].apply(extract_retail_insight)
cot_results_df_v2['retail_insight_polarity'] = cot_results_df_v2['retail_insight_text'].apply(lambda x: TextBlob(str(x)).sentiment.polarity)
insight_polarities['CoT V2'] = cot_results_df_v2['retail_insight_polarity'].mean()

# --- Display and Plot Results ---

polarity_df = pd.DataFrame(insight_polarities.items(), columns=['Version', 'Average_Polarity'])

print("\n--- Average Retail Insight Polarity by Version ---")
display(polarity_df)

plt.figure(figsize=(10, 6))
sns.barplot(x='Version', y='Average_Polarity', data=polarity_df, palette='viridis')
plt.title('Average TextBlob Polarity of Retail Insights by Prompting Technique Version')
plt.xlabel('Prompting Technique Version')
plt.ylabel('Average Polarity (TextBlob)')
plt.ylim(-1, 1) # Polarity ranges from -1 to 1
plt.xticks(rotation=45, ha='right')
plt.grid(axis='y', linestyle='--', alpha=0.7)
plt.tight_layout()
plt.show()

--- Average Retail Insight Polarity by Version ---

from wordcloud import WordCloud, STOPWORDS
import matplotlib.pyplot as plt

# Define common stopwords for WordCloud
stopwords = set(STOPWORDS)
# Add any domain-specific stopwords if necessary (e.g., 'review', 'customer', 'product')
stopwords.update(['customer', 'consider', 'customers']) # Added 'customer' and 'consider'

insight_texts = {
    'Zero-Shot V1': " ".join(zero_shot_results_df['retail_insight_text'].dropna().astype(str)),
    'Zero-Shot V2': " ".join(zero_shot_results_df_v2['retail_insight_text'].dropna().astype(str)),
    'Few-Shot V1': " ".join(few_shot_results_df1['retail_insight_text'].dropna().astype(str)),
    'Few-Shot V2': " ".join(few_shot_results_df_v2['retail_insight_text'].dropna().astype(str)),
    'CoT V1': " ".join(cot_results_df['retail_insight_text'].dropna().astype(str)),
    'CoT V2': " ".join(cot_results_df_v2['retail_insight_text'].dropna().astype(str)),
}

for version, text in insight_texts.items():
    plt.figure(figsize=(10, 6)) # Create a new figure for each word cloud
    if text:
        wordcloud = WordCloud(
            stopwords=stopwords,
            background_color='white',
            max_words=100,
            collocations=False,
            width=800,
            height=400
        ).generate(text)

        plt.imshow(wordcloud, interpolation='bilinear')
        plt.title(f'Retail Insights - {version}', color='black')
        plt.axis('off')
    else:
        plt.text(0.5, 0.5, f'No insights for {version}', horizontalalignment='center', verticalalignment='center', fontsize=12, color='black')
        plt.title(f'Retail Insights - {version}', color='black')
        plt.axis('off')
    plt.tight_layout()
    plt.show()

	raw_llm_output	original_user_content	review_index
0	```json\n{\n "categories": {\n "division":...	Division: General\nDepartment: Bottoms\nClass:...	16753
1	```json\n{\n "categories": {\n "division":...	Division: General\nDepartment: Tops\nClass: Kn...	2642
2	```json\n{\n "categories": {\n "division":...	Division: General Petite\nDepartment: Tops\nCl...	3548
3	```json\n{\n "categories": {\n "division":...	Division: General\nDepartment: Bottoms\nClass:...	3163
4	```json\n{\n "categories": {\n "division":...	Division: General\nDepartment: Dresses\nClass:...	22121

	raw_llm_output	original_user_content	review_index
0	```json\n{\n "categories": {\n "division":...	Division: General\nDepartment: Bottoms\nClass:...	16753
1	```json\n{\n "categories": {\n "division":...	Division: General\nDepartment: Tops\nClass: Kn...	2642
2	```json\n{\n "categories": {\n "division":...	Division: General Petite\nDepartment: Tops\nCl...	3548
3	```json\n{\n "categories": {\n "division":...	Division: General\nDepartment: Bottoms\nClass:...	3163
4	```json\n{\n "categories": {\n "division":...	Division: General\nDepartment: Dresses\nClass:...	22121
5	```json\n{\n "categories": {\n "division":...	Division: General Petite\nDepartment: Bottoms\...	2576
6	```json\n{\n "categories": {\n "division":...	Division: General\nDepartment: Tops\nClass: Bl...	9330
7	```json\n{\n "categories": {\n "division":...	Division: Initmates\nDepartment: Intimate\nCla...	21298
8	```json\n{\n "categories": {\n "division":...	Division: General\nDepartment: Tops\nClass: Fi...	9183
9	```json\n{\n "categories": {\n "division":...	Division: General Petite\nDepartment: Tops\nCl...	9647
10	```json\n{\n "categories": {\n "division":...	Division: General Petite\nDepartment: Tops\nCl...	14448
11	```json\n{\n "categories": {\n "division":...	Division: General Petite\nDepartment: Dresses\...	18857
12	```json\n{\n "categories": {\n "division":...	Division: General\nDepartment: Dresses\nClass:...	6503
13	```json\n{\n "categories": {\n "division":...	Division: General Petite\nDepartment: Tops\nCl...	13369
14	```json\n{\n "categories": {\n "division":...	Division: Initmates\nDepartment: Intimate\nCla...	6738
15	```json\n{\n "categories": {\n "division":...	Division: General\nDepartment: Tops\nClass: Bl...	4619
16	```json\n{\n "categories": {\n "division":...	Division: General Petite\nDepartment: Tops\nCl...	10329
17	```json\n{\n "categories": {\n "division":...	Division: General\nDepartment: Bottoms\nClass:...	15761
18	```json\n{\n "categories": {\n "division":...	Division: General Petite\nDepartment: Dresses\...	12942
19	```json\n{\n "categories": {\n "division":...	Division: General Petite\nDepartment: Tops\nCl...	17748

	raw_llm_output	original_user_content	review_index	parsed_output	extracted_categories	extracted_sentiment	extracted_summary	extracted_personalized_message	extracted_retail_insight
0	```json\n{\n "categories": {\n "division":...	Division: General\nDepartment: Bottoms\nClass:...	16753	{'categories': {'division': 'General', 'depart...	{'division': 'General', 'department': 'Bottoms...	positive	The reviewer is highly satisfied with the fit ...	Thank you for your wonderful review! We're thr...	Consider providing care instructions and durab...
1	```json\n{\n "categories": {\n "division":...	Division: General\nDepartment: Tops\nClass: Kn...	2642	{'categories': {'division': 'General', 'depart...	{'division': 'General', 'department': 'Tops', ...	positive	The reviewer loves the versatility and comfort...	Thank you for your wonderful review! We're thr...	Consider promoting the versatility of the prod...
2	```json\n{\n "categories": {\n "division":...	Division: General Petite\nDepartment: Tops\nCl...	3548	{'categories': {'division': 'General Petite', ...	{'division': 'General Petite', 'department': '...	positive	The reviewer loves the sweater for its chic an...	Thank you for your wonderful review! We're thr...	Consider promoting the versatility of the swea...
3	```json\n{\n "categories": {\n "division":...	Division: General\nDepartment: Bottoms\nClass:...	3163	{'categories': {'division': 'General', 'depart...	{'division': 'General', 'department': 'Bottoms...	positive	The reviewer loves the jeans for their true-to...	Thank you for your wonderful feedback! We're t...	Highlight the unique features of the jeans, su...
4	```json\n{\n "categories": {\n "division":...	Division: General\nDepartment: Dresses\nClass:...	22121	{'categories': {'division': 'General', 'depart...	{'division': 'General', 'department': 'Dresses...	positive	The reviewer expresses strong appreciation for...	Thank you for your wonderful feedback! We're t...	Consider expanding the range of petite sizes a...

	Categories_Description	Sentiment_Description	Summary_Description	Personalized_Message_Description	Retail_Insight_Description
0	- The categories provided (division, departmen...	- The sentiment is correctly identified as "po...	- The summary captures the main points of the ...	- The personalized message is warm and acknowl...	Description not found
1	- The categories provided (division, departmen...	- The sentiment is correctly identified as "po...	- The summary captures the main points of the ...	- The personalized message is warm and acknowl...	- The retail insight is practical and relevant...
2	- The categories provided (division, departmen...	- The sentiment is correctly identified as "po...	- The summary captures the main points of the ...	- The personalized message is warm and acknowl...	Description not found
3	- The categories provided (division, departmen...	- The sentiment is labeled as "positive," whic...	- The summary effectively encapsulates the mai...	- The personalized message is warm and appreci...	Description not found
4	- The categories provided (division, departmen...	- The sentiment is correctly identified as "po...	- The summary captures the essence of the revi...	- The personalized message is warm and acknowl...	Description not found

	Categories_Description	Sentiment_Description	Summary_Description	Personalized_Message_Description	Retail_Insight_Description
0	- The categories provided (division, departmen...	- The sentiment is correctly identified as "po...	- The summary captures the main points of the ...	- The personalized message is warm and acknowl...	Description not found
1	- The categories provided (division, departmen...	- The sentiment is correctly identified as "po...	- The summary captures the main points of the ...	- The personalized message is warm and acknowl...	- The retail insight is practical and relevant...
2	- The categories provided (division, departmen...	- The sentiment is correctly identified as "po...	- The summary captures the main points of the ...	- The personalized message is warm and acknowl...	Description not found
3	- The categories provided (division, departmen...	- The sentiment is labeled as "positive," whic...	- The summary effectively encapsulates the mai...	- The personalized message is warm and appreci...	Description not found
4	- The categories provided (division, departmen...	- The sentiment is correctly identified as "po...	- The summary captures the essence of the revi...	- The personalized message is warm and acknowl...	Description not found
5	- The categories provided (division, departmen...	- The sentiment is labeled as "negative," whic...	- The summary captures the main points of the ...	- The personalized message is appropriate and ...	Description not found
6	- The categories provided (division, departmen...	- The sentiment is correctly identified as "ne...	- The summary accurately captures the main poi...	- The personalized message is appropriate and ...	Description not found
7	- The categories provided (division, departmen...	- The sentiment is correctly identified as "po...	- The summary accurately captures the main poi...	- The personalized message is appropriate and ...	Description not found
8	- The categories provided (division, departmen...	- The sentiment is labeled as "neutral," which...	- The summary accurately reflects the main poi...	- The personalized message is well-crafted, ac...	Description not found
9	- The categories provided (division, departmen...	- The sentiment is correctly identified as "ne...	- The summary captures the main points of the ...	- The personalized message is appropriate and ...	Description not found
10	- The output correctly identifies the division...	- The sentiment is labeled as "neutral," which...	- The summary captures the main points of the ...	- The personalized message is warm and acknowl...	- The retail insight provides a constructive s...
11	- The categories provided (division, departmen...	- The sentiment is correctly identified as "po...	- The summary captures the main points of the ...	- The personalized message is appropriate and ...	Description not found
12	- The categories provided (division, departmen...	- The sentiment is correctly identified as "po...	- The summary captures the main points of the ...	- The personalized message is warm and engagin...	Description not found
13	- The categories provided (division, departmen...	- The sentiment is correctly identified as "po...	- The summary accurately captures the essence ...	- The personalized message is warm and appreci...	Description not found
14	- The categories provided (division, departmen...	- The sentiment is labeled as "neutral," which...	- The summary accurately captures the essence ...	- The personalized message is appropriate and ...	Description not found
15	- The categories provided (division, departmen...	- The sentiment is correctly identified as "po...	- The summary captures the main points of the ...	- The personalized message is appropriate and ...	Description not found
16	- The categories provided (division, departmen...	- The sentiment is correctly identified as "ne...	- The summary effectively encapsulates the mai...	- The personalized message is appropriate and ...	- The retail insight provides a valuable sugge...
17	- The categories provided (division, departmen...	- The sentiment is labeled as "negative," whic...	- The summary effectively encapsulates the mai...	- The personalized message is empathetic and a...	Description not found
18	- The categories provided (division, departmen...	- The sentiment is correctly identified as "po...	- The summary accurately captures the essence ...	- The personalized message is appropriate and ...	Description not found
19	- The categories provided (division, departmen...	- The sentiment is labeled as "negative," whic...	- The summary accurately captures the key poin...	- The personalized message is empathetic and a...	Description not found

Real-Time Retail Feedback Intelligence¶

Installing and Importing Necessary Libraries¶

Data Loading¶

Loading and Understanding the Data¶

Sanity checks¶

Data Overview¶

Data Cleaning and Preprocessing¶

Exploratory Data Analysis¶

Univariate¶

Bivariate¶

Text Analysis¶

Building the Generative AI Pipeline¶

Sanity Check¶

Prompt Engineering and Evaluation¶

The Models¶

Zero-Shot Example¶

LLM as a Judge for Zero-Shot¶

Technique 2: Few-Shot Prompting¶

Few-Shot Example¶

LLM as a Judge for Few-Shot¶

Technique 3: Chain-of-Thought (CoT) Prompting¶

Applying GenAI for Product Recommendation:¶

Accuracy Score, Confusion Matrix¶

Comparison of Prompting Techniques:¶

Observations and Insights¶

Generating Actionable Product Improvement Suggestions¶

Short-Term Actionable Recommendations:¶

Long-Term actionable Business Recommendations:¶

Observations and Insights¶

Conclusion¶

	Unnamed: 0	Clothing.ID	Age	Title	Review.Text	Rating	Recommended.IND	Positive.Feedback.Count	Division.Name	Department.Name	Class.Name
0	1	767	33	NaN	Absolutely wonderful - silky and sexy and comf...	4	1	0	Initmates	Intimate	Intimates
1	2	1080	34	NaN	Love this dress! it's sooo pretty. i happene...	5	1	4	General	Dresses	Dresses
2	3	1077	60	Some major design flaws	I had such high hopes for this dress and reall...	3	0	0	General	Dresses	Dresses
3	4	1049	50	My favorite buy!	I love, love, love this jumpsuit. it's fun, fl...	5	1	0	General Petite	Bottoms	Pants
4	5	847	47	Flattering shirt	This shirt is very flattering to all due to th...	5	1	6	General	Tops	Blouses

	Unnamed: 0	Clothing.ID	Age	Title	Review.Text	Rating	Recommended.IND	Positive.Feedback.Count	Division.Name	Department.Name	Class.Name
0	1	767	33	My Review	Absolutely wonderful - silky and sexy and comf...	4	1	0	Initmates	Intimate	Intimates
1	2	1080	34	My Review	Love this dress! it's sooo pretty. i happene...	5	1	4	General	Dresses	Dresses
2	3	1077	60	Some major design flaws	I had such high hopes for this dress and reall...	3	0	0	General	Dresses	Dresses
3	4	1049	50	My favorite buy!	I love, love, love this jumpsuit. it's fun, fl...	5	1	0	General Petite	Bottoms	Pants
4	5	847	47	Flattering shirt	This shirt is very flattering to all due to th...	5	1	6	General	Tops	Blouses

	0
Unnamed: 0	0
Clothing.ID	0
Age	0
Title	0
Review.Text	845
Rating	0
Recommended.IND	0
Positive.Feedback.Count	0
Division.Name	14
Department.Name	14
Class.Name	14

	index	Clothing.ID	Age	Title	Review.Text	Rating	Recommended.IND	Positive.Feedback.Count	Division.Name	Department.Name	Class.Name
18491	18491	1078	29	Disappointed--will return	I ordered this dress online in order to get a ...	3	0	1	General	Dresses	Dresses
18119	18119	862	34	A pop of color and style	Pros:\r\n- fits tts. i tried on a size m and i...	4	1	11	General Petite	Tops	Knits
9559	9559	860	47	Beautiful blouse	I purchased the green in xs and it fits perfec...	5	1	0	General	Tops	Knits
6444	6444	292	35	Make your buns look adorable	Love this brand and i keep coming back. i don'...	5	1	0	Initmates	Intimate	Swim
2360	2360	895	45	Pretty party piece	I'll be wearing this to holiday parties this y...	4	1	0	General Petite	Tops	Fine gauge

	categories	sentiment	summary	personalized_message	retail_insight
0	1.0	1.0	1.0	1.00	1.0
1	1.0	1.0	1.0	0.90	1.0
2	1.0	1.0	1.0	0.75	1.0
3	1.0	1.0	1.0	1.00	1.0
4	1.0	1.0	1.0	1.00	1.0

	review_index	categories_score	sentiment_score	summary_score	personalized_message_score	retail_insight_score
0	11956	0.80	0.25	0.7	0.75	0.75
1	8421	0.75	1.00	1.0	0.80	1.00
2	12715	0.75	0.25	1.0	0.75	0.50
3	18326	0.80	1.00	0.5	0.50	0.75
4	17154	0.75	1.00	1.0	0.50	0.75

	review_index	categories_desc	sentiment_desc	summary_desc	personalized_message_desc	retail_insight_desc
0	11956	- The categories provided (Division, Departmen...	- The review expresses a mix of positive and n...	- The summary provided by the zero-shot model ...	Description not found	Description not found
1	8421	- The categories provided (Division, Departmen...	- The sentiment is correctly identified as "po...	- The summary provided by the zero-shot model ...	Description not found	Description not found
2	12715	- The categories provided (Division, Departmen...	- The sentiment in the review is mixed. The re...	Description not found	Description not found	Description not found
3	18326	- The categories provided ("Division", "Depart...	- The sentiment output is "positive," which al...	- The summary provided captures some key point...	Description not found	- The retail insight provided suggests emphasi...
4	17154	- The categories provided (Division, Departmen...	- The sentiment "positive" accurately reflects...	Description not found	Description not found	Description not found

	index	Clothing.ID	Age	Rating	Positive.Feedback.Count	Recom
count	22628.000000	22628.000000	22628.000000	22628.000000	22628.000000	22628.000000
mean	11313.500000	919.695908	43.282880	4.183092	2.631784	0.818764
std	6532.285282	201.683804	12.328176	1.115911	5.787520	0.385222
min	0.000000	1.000000	18.000000	1.000000	0.000000	0.000000
25%	5656.750000	861.000000	34.000000	4.000000	0.000000	1.000000
50%	11313.500000	936.000000	41.000000	5.000000	1.000000	1.000000
75%	16970.250000	1078.000000	52.000000	5.000000	3.000000	1.000000
max	22627.000000	1205.000000	99.000000	5.000000	122.000000	1.000000

	Original_Index	Clothing.ID	Age	Title	Review.Text	Rating	Positive.Feedback.Count	Division.Name	Department.Name	Class.Name	Recom	LLM_Recommended_Raw	LLM_Recommended_Flag	LLM_Recommend_Reason
0	8037	1103	47	Change armpits	I love, love this dress except for the armpits...	4	0	General	Dresses	Dresses	1	Score: 0 \nReason: The reviewer expresses str...	0	The reviewer expresses strong dissatisfaction ...
1	17288	909	31	Awkward sweater	I wanted this sweater to work but sadly it fai...	2	0	General	Tops	Fine gauge	0	Score: 0 \nReason: The reviewer expresses dis...	0	The reviewer expresses disappointment with the...
2	2085	872	35	Best. tee. ever.	Oh my! i love this tee. it is super soft. i lo...	5	0	General Petite	Tops	Knits	1	Score: 1 \nReason: The reviewer expresses str...	1	The reviewer expresses strong satisfaction wit...
3	11051	701	32	Well-made but lacks structure	I love the style of this swimsuit on the model...	3	5	Initmates	Intimate	Swim	1	Score: 0 \nReason: The reviewer appreciated t...	0	The reviewer appreciated the style of the swim...
4	13868	1078	61	Strangely cut	Was super excited to try this on, but had to g...	2	1	General Petite	Dresses	Dresses	0	Score: 0 \nReason: The reviewer experienced s...	0	The reviewer experienced significant sizing is...

	Original_Index	Actual_Recommendation	LLM_Recommended_Reason	Explanation_of_Mismatch
0	8037	1	The reviewer expresses strong dissatisfaction ...	The LLM's prediction of a flag (0) suggests th...
1	11051	1	The reviewer appreciated the style of the swim...	The LLM's prediction of a flag (0) suggests th...
2	4821	1	The review indicates dissatisfaction with the ...	The LLM's prediction of a flag of 0 suggests t...
3	21198	1	The reviewer had a negative experience with th...	The LLM's prediction of a flag (0) suggests th...

extracted_sentiment	positive	neutral	negative
Zero-Shot V1	0.6	0.2	0.2
Zero-Shot V2	0.2	0.2	0.6
Few-Shot V1	0.6	0.2	0.2
Few-Shot V2	0.2	0.0	0.4
CoT V1	0.8	0.0	0.2
CoT V2	0.2	0.4	0.4

	Version	Average_Polarity
0	Zero-Shot V1	0.204116
1	Zero-Shot V2	0.383810
2	Few-Shot V1	0.176667
3	Few-Shot V2	0.220000
4	CoT V1	0.013500
5	CoT V2	0.195771